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J.S., Ashwin, and Manoharan N. "Audio Denoising Based on Short Time Fourier Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 1 (2018): 89–92. https://doi.org/10.11591/ijeecs.v9.i1.pp89-92.
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This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio denoising technique based on Short Time Fourier Transform (STFT) is implemented. The proposed architecture uses a novel approach to estimate environmental noise from speech adaptively. Here original speech signals are given as input signal. Using AWGN, noises are added to the signal. Then noised signals are de-noised using STFT techniques. Finally Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR) values for noised and denoised signals are obtained.
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S. Ashwin, J., and N. Manoharan. "Audio Denoising Based on Short Time Fourier Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 1 (2018): 89. http://dx.doi.org/10.11591/ijeecs.v9.i1.pp89-92.
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<p>This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio de-noising technique based on Short Time Fourier Transform (STFT) is implemented. The proposed architecture uses a novel approach to estimate environmental noise from speech adaptively. Here original speech signals are given as input signal. Using AWGN, noises are added to the signal. Then noised signals are de-noised using STFT techniques. Finally Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR) values for noised and de-noised signals are obtained.</p>
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C, Shraddha, Chayadevi M L, Anusuya M A, and Vani H Y. "Enhancing Noise Reduction with Bionic Wavelet and Adaptive Filtering." Inteligencia Artificial 27, no. 74 (2024): 214–26. http://dx.doi.org/10.4114/intartif.vol27iss74pp214-226.
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Speech signals often contain different forms of background and environmental noise. For the development of an efficient speech recognition system, it is essential to preprocess noisy speech signals to reduce the impact of these disturbances. Notably, prior research has paid limited attention to pink and babble noises. This gap in knowledge inspired us to develop and implement hybrid algorithms tailored to handle these specific noise types. We introduce a hybrid method that combines the Bionic Wavelet transform with Adaptive Filtering to enhance signal strength. The performance of this method is assessed using various metrics, including Mean Squared Error, Signal-to-Noise Ratio, and Peak Signal-to-Noise Ratio. Notably, our findings indicate that SNR and PSNR metrics are especially effective in enhancing the handling of pink and babble noises.
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Kwon, Ju Hyeok, So Eui Kim, Na Hye Kim, Eui Chul Lee, and Jee Hang Lee. "Preeminently Robust Neural PPG Denoiser." Sensors 22, no. 6 (2022): 2082. http://dx.doi.org/10.3390/s22062082.
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Photoplethysmography (PPG) is a simple and cost-efficient technique that effectively measures cardiovascular response by detecting blood volume changes in a noninvasive manner. A practical challenge in the use of PPGs in real-world applications is noise reduction. PPG signals are likely to be compromised by various types of noise, such as scattering or motion artifacts, and removing such compounding noises using a monotonous method is not easy. To this end, this paper proposes a neural PPG denoiser that can robustly remove multiple types of noise from a PPG signal. By casting the noise reduction problem into a signal restoration approach, we aim to achieve a solid performance in the reduction of different noise types using a single neural denoiser built upon transformer-based deep generative models. Using this proposed method, we conducted the experiments on the noise reduction of a PPG signal synthetically contaminated with five types of noise. Following this, we performed a comparative study using six different noise reduction algorithms, each of which is known to be the best model for each noise. Evaluation results of the peak signal-to-noise ratio (PSNR) show that the neural PPG denoiser is superior in three out of five noise types to the performance of conventional noise reduction algorithms. The salt-and-pepper noise type showed the best performance, with the PSNR of the neural PPG denoiser being 36.6080, and the PSNRs of the other methods were 19.8160 and 32.8234. The Poisson noise type performed the worst, showing a PSNR of 33.0090; the PSNRs of other methods were 35.1822 and 33.4795, respectively. Thereafter, an experiment to recover a signal synthesized with two or more of the five noise types was conducted. When the number of mixed noises was two, three, four, and five, the PSNRs were 29.2759, 27.8759, 26.5608, and 25.9402, respectively. Finally, an experiment to recover motion artifacts was also conducted. The synthesized motion artifact signal was created by synthesizing only a certain ratio of the total signal length. As a result of the motion artifact signal restoration, the PSNRs were 25.2872, 22.8240, 21.2901, and 19.9577 at 30%, 50%, 70%, and 90% motion artifact ratios, respectively. In the three experiments conducted, the neural PPG denoiser showed that various types of noise were effectively removed. This proposal contributes to the universal denoising of continuous PPG signals and can be further expanded to denoise continuous signals in the general domain.
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Malleswari, Pinjala N., Ch Hima Bindu, and K. Satya Prasad. "An Improved Denoising of Electrocardiogram Signals Based on Wavelet Thresholding." Journal of Biomimetics, Biomaterials and Biomedical Engineering 51 (June 14, 2021): 117–29. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.51.117.
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Electrocardiogram (ECG) is the most important signal in the biomedical field for the diagnosis of Cardiac Arrhythmia (CA). ECG signal often interrupted with various noises due to non-stationary nature which leads to poor diagnosis. Denoising process helps the physicians for accurate decision making in treatment. In many papers various noise elimination techniques are tried to enhance the signal quality. In this paper a novel hybrid denoising technique using EMD-DWT for the removal of various noises such as Additive White Gaussian Noise (AWGN), Baseline Wander (BW) noise, Power Line Interference (PLI) noise at various concentrations are compared to the conventional methods in terms of Root Mean Square Error (RSME), Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Cross-Correlation (CC) and Percent Root Square Difference (PRD). The average values of RMSE, SNR, PSNR, CC and PRD are 0.0890, 9.8821, 14.4464, 0.9872 and 10.9036 for the EMD approach, respectively, and 0.0707, 10.7181, 16.2824, 0.9874 and 10.7245 for the proposed EMD-DWT approach, respectively, by removing AWGN noise. Similarly BW noise and PLI are removed from the ECG signal by calculating the same quality metrics. The proposed methodology has lower RMSE and PRD values, higher SNR, PSNR and CC values than the conventional methods.
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Sonali, Malviya, and Anshuj Jain Prof. "Analysis PSNR of High Density Salt and Pepper Impulse Noise Using Median Filter." International Journal of Trend in Scientific Research and Development 3, no. 1 (2018): 866–70. https://doi.org/10.31142/ijtsrd19086.
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In this paper a new method for the enhancement of gray scale images is introduced, when images are corrupted by fixed valued impulse noise salt and pepper noise . The proposed methodology ensures a better output for low and medium density of fixed value impulse noise as compare to the other famous filters like Standard Median Filter SMF , Decision Based Median Filter DBMF and Modified Decision Based Median Filter MDBMF etc. The main objective of the proposed method was to improve peak signal to noise ratio PSNR , visual perception and reduction in blurring of image. The proposed algorithm replaced the noisy pixel by trimmed mean value. When previous pixel values, 0's and 255's are present in the particular window and all the pixel values are 0's and 255's then the remaining noisy pixels are replaced by mean value. The gray scale image of mandrill and Lena were tested via proposed method. The experimental result shows better peak signal to noise ratio PSNR , mean square error MSE and mean absolute error MAE values with better visual and human perception. Sonali Malviya | Prof. Anshuj Jain "Analysis PSNR of High Density Salt and Pepper Impulse Noise Using Median Filter" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19086.pdf
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Mardiah, Ainil, Sri Hartati, and Agus Sihabuddin. "Face Image Generation and Enhancement Using Conditional Generative Adversarial Network." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 16, no. 1 (2022): 1. http://dx.doi.org/10.22146/ijccs.58327.
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The accuracy and speed of a single image super-resolution using a convolutional neural network is often a problem in improving finer texture details when using large enhancement factors. Some recent studies have focused on minimal mean square error, resulting in a high peak signal to noise ratio. Generally, although the peak signal to noise ratio has a high value, the output image is less detailed. This shows that the determination of super-resolution is not optimal. Conditional Generative Adversarial Network based on Boundary Equilibrium Generative Adversarial Network, by combining Mean Square Error Loss and GAN Loss as a loss function to optimize the super-resolution model and produce super-resolution images. Also, the generator network is designed with skip connection architecture to increase convergence speed and strengthen feature distribution. Image quality value parameters used in this study are Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The results showed the highest image quality values using dataset validation were 26.55 for PSNR values and 0.93 for SSIM values. The highest image quality values using the testing dataset are 24.56 for the PSNR value and 0.91 for the SSIM value.
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Rao*, G. Manmadha, Raidu Babu D.N, Krishna Kanth P.S.L, Vinay B., and Nikhil V. "Reduction of Impulsive Noise from Speech and Audio Signals by using Sd-Rom Algorithm." International Journal of Recent Technology and Engineering 10, no. 1 (2021): 265–68. http://dx.doi.org/10.35940/ijrte.a5943.0510121.
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Removal of noise is the heart for speech and audio signal processing. Impulse noise is one of the most important noise which corrupts different parts in speech and audio signals. To remove this type of noise from speech and audio signals the technique proposed in this work is signal dependent rank order mean (SD-ROM) method in recursive version. This technique is used to replace the impulse noise samples based on the neighbouring samples. It detects the impulse noise samples based on the rank ordered differences with threshold values. This technique doesn’t change the features and tonal quality of signal. Rank ordered differences is used for detecting the impulse noise samples in speech and audio signals. Once the sample is detected as corrupted sample, that sample is replaced with rank ordered mean value and this rank ordered mean value depends on the sliding window size and neighbouring samples. This technique shows good results in terms of signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) when compared with other techniques. It mainly used for removal of impulse noises from speech and audio signals.
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G.Manmadha, Rao, Raidu Babu D.N, Krishna Kanth P.S.L, B.Vinay, and V.Nikhil. "Reduction of Impulsive Noise from Speech and Audio Signals by using Sd-Rom Algorithm." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 1 (2021): 265–68. https://doi.org/10.35940/ijrte.A5943.0510121.
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Removal of noise is the heart for speech and audio signal processing. Impulse noise is one of the most important noise which corrupts different parts in speech and audio signals. To remove this type of noise from speech and audio signals the technique proposed in this work is signal dependent rank order mean (SD-ROM) method in recursive version. This technique is used to replace the impulse noise samples based on the neighbouring samples. It detects the impulse noise samples based on the rank ordered differences with threshold values. This technique doesn’t change the features and tonal quality of signal. Rank ordered differences is used for detecting the impulse noise samples in speech and audio signals. Once the sample is detected as corrupted sample, that sample is replaced with rank ordered mean value and this rank ordered mean value depends on the sliding window size and neighbouring samples. This technique shows good results in terms of signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) when compared with other techniques. It mainly used for removal of impulse noises from speech and audio signals.
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Zhu, You Lian, and Cheng Huang. "Median Morphological Filter Design Based on the PSO Algorithm." Applied Mechanics and Materials 128-129 (October 2011): 181–84. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.181.
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Design of morphological filter greatly depends on morphological operations and structuring elements selection. A filter design method used median closing morphological operation is proposed to enhance the image denoising ability and the PSO algorithm is introduced for structural elements selecting. The method takes the peak value signal-to-noise ratio (PSNR) as the cost function and may adaptively build unit structuring elements with zero square matrix. Experimental results show the proposed method can effectively remove impulse noise from a noisy image, especially from a low signal-to-noise ratio (SNR) image; the noise reduction performance has obvious advantages than the other.
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Prasad, K. D., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Techniques for Improving PSNR of Liver Images by Median Filtering over Mask Filtering." CARDIOMETRY, no. 25 (February 14, 2023): 990–95. http://dx.doi.org/10.18137/cardiometry.2022.25.990-995.
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Aim: The goal of this research is to employ median filters and mask filters to reduce noise in liver images so that they can be improved. In addition, the Peak Signal to Noise Ratio of both filters’ outputs was examined (PSNR). Materials and Methods: The afflicted and normal liver photos were obtained from the Kaggle website for this investigation. Then, using Matlab software, the mask filtering and median filtering algorithms were run. Clinicalc. com was used to compute sample size, and SPSS software was used to conduct the comparative analysis. This study has two groups, each with a sample size of 20 people with an average G power of 80%. The innovative median filter’s performance is assessed, and the PSNR performance metric is compared to that of the mask filter. Results: The PSNR of innovative median filters is 64.0310, while mask filters have a PSNR of 78.0095, according to Matlab simulation data. The significant value of PSNR (Peak Signal to Noise Ratio) (0.409) and p>0.05 was found in the statistical analysis. Conclusion: The innovative median filter delivers greater PSNR than the mask filter for medical image enhancement on ultrasound liver pictures, according to this study.
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Naz, F., D. J. Rani, and R. Rajakumari. "Detection and comparison of Diabetic Maculopathy using C-Means Clustering Algorithm and Watershed Algorithm." CARDIOMETRY, no. 25 (February 14, 2023): 845–51. http://dx.doi.org/10.18137/cardiometry.2022.25.845851.
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Aim: The aim of this research work is for the presence of Novel Diabetic Maculopathy Detection using modern algorithms, and comparing the Peak Signal to Noise Ratio (PSNR) between the C-Means clustering Algorithms and Watershed Algorithm. Materials and Methods: The sample images were taken from kaggle’s website. Samples were considered as (N=24) for C-Means Clustering Algorithm and (N=24) for Watershed algorithm in accordance with total sample size calculated using clinicalc.com by keeping alpha error-threshold value 0.05, enrollment ratio as 0.1, 95% confidence interval, G power as 80%. The Peak Signal to Noise Ratio was calculated by using the MATLAB Programming with a standard data set. Results: Comparison of PSNR is done by independent sample t-test using SPSS software. There is a statistical insignificant difference between C-Means Clustering Algorithm and Watershed algorithm with p=0.11, p>0.05 (PSNR = 35.3411) showed better results in comparison to Watershed Algorithm (PSNR =9.7420). Conclusion: C-Means Clustering Algorithms were found to give higher PSNR than in Watershed Algorithms for the Novel Diabetic Maculopathy Detection.
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Zhang, Huage, Jinfei Yu, Yumei Ma, Zhenkuan Pan, and Jingjing Zhao. "Image Restoration Based on Stochastic Resonance in a Parallel Array of Fitzhugh–Nagumo Neuron." Complexity 2020 (November 11, 2020): 1–9. http://dx.doi.org/10.1155/2020/8843950.
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The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo (FHN) neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio (PSNR) conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal was converted into a binary pulse amplitude modulation (BPAM) signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance (SR). Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity (SSIM) index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment.
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Veeramakal, T., Syed Raffi Ahamed J, and Bagiyalakshmi N. "Speech Signal Enhancement with Integrated Weighted Filtering for PSNR Reduction in Multimedia Applications." Journal of Computer Allied Intelligence 2, no. 3 (2024): 1–14. http://dx.doi.org/10.69996/jcai.2024011.
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This paper investigates the effectiveness of the Weighted Kalman Integrated Band Rejection (WKBR) method for enhancing speech signals in multimedia applications. Speech enhancement is crucial for improving the quality and intelligibility of audio in environments with varying noise types and levels. The WKBR method is evaluated across ten different noise scenarios, including white noise, babble noise, street noise, airplane cabin noise, and more. Performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Short-Time Objective Intelligibility (STOI) are used to quantify the enhancement. The results show significant improvements, with PSNR increasing from an average of 12.8 dB before enhancement to 21.9 dB after enhancement, MSE reducing from an average of 0.0179 to 0.0053, and STOI scores improving from an average of 0.58 to 0.75. These findings highlight the potential of WKBR as a powerful tool for speech signal enhancement, making it a promising solution for real-world multimedia applications where clear and intelligible speech is essential.
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Yunus, Mahmuddin, and Agus Harjoko. "Penyembunyian Data pada File Video Menggunakan Metode LSB dan DCT." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 8, no. 1 (2014): 81. http://dx.doi.org/10.22146/ijccs.3498.
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AbstrakPenyembunyian data pada file video dikenal dengan istilah steganografi video. Metode steganografi yang dikenal diantaranya metode Least Significant Bit (LSB) dan Discrete Cosine Transform (DCT). Dalam penelitian ini dilakukan penyembunyian data pada file video dengan menggunakan metode LSB, metode DCT, dan gabungan metode LSB-DCT. Sedangkan kualitas file video yang dihasilkan setelah penyisipan dihitung dengan menggunakan Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR).Uji eksperimen dilakukan berdasarkan ukuran file video, ukuran file berkas rahasia yang disisipkan, dan resolusi video.Hasil pengujian menunjukkan tingkat keberhasilan steganografi video dengan menggunakan metode LSB adalah 38%, metode DCT adalah 90%, dan gabungan metode LSB-DCT adalah 64%. Sedangkan hasil perhitungan MSE, nilai MSE metode DCT paling rendah dibandingkan metode LSB dan gabungan metode LSB-DCT. Sedangkan metode LSB-DCT mempunyai nilai yang lebih kecil dibandingkan metode LSB. Pada pengujian PSNR diperoleh databahwa nilai PSNR metode DCTlebih tinggi dibandingkan metode LSB dan gabungan metode LSB-DCT. Sedangkan nilai PSNR metode gabungan LSB-DCT lebih tinggi dibandingkan metode LSB. Kata Kunci—Steganografi, Video, Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) AbstractHiding data in video files is known as video steganography. Some of the well known steganography methods areLeast Significant Bit (LSB) and Discrete Cosine Transform (DCT) method. In this research, data will be hidden on the video file with LSB method, DCT method, and the combined method of LSB-DCT. While the quality result of video file after insertion is calculated using the Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The experiments were conducted based on the size of the video file, the file size of the inserted secret files, and video resolution.The test results showed that the success rate of the video steganography using LSB method was 38%, DCT method was 90%, and the combined method of LSB-DCT was 64%. While the calculation of MSE, the MSE method DCT lower than the combined method of LSB and LSB-DCT method. While LSB-DCT method has asmaller value than the LSB method. The PNSR experiment showed that the DCT method PSNR value is higher than the combined method of LSB and LSB-DCT method. While PSNR combined method LSB-DCT higher compared LSB method. Keywords—Steganography, Video, Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR)
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Vani, A. "Implementation of Efficient Adaptive Noise Cancellation on FPGA Using LMS and LLMS Algorithms." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2849–53. https://doi.org/10.22214/ijraset.2025.70797.
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This work focuses on the implementation of an efficient adaptive noise cancellation system using FPGA technology based on LMS (Least Mean Squares) and LLMS (Leaky LMS) algorithms. The aim is to enhance real-time audio signal processing by mitigating unwanted noise effectively. Initially, MATLAB was used to process noisy and clean audio signals, which were then simulated using Verilog in Xilinx ISE 14.7. The LMS and LLMS algorithms were implemented and compared for their performance in noise cancellation. The LLMS algorithm demonstrated superior performance, achieving significant improvements in Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and stability compared to the traditional LMS approach. This project demonstrates the practical applicability of LLMS-based ANC systems for embedded, low-power, real-time noise cancellation applications. Future work includes deploying the system on physical FPGA boards, optimizing resource usage further, and testing under dynamic noise environments to validate real-world performance.
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Yongjie Peng. "Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter." Journal of Electrical Systems 20, no. 3s (2024): 2491–500. http://dx.doi.org/10.52783/jes.3148.
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The most significant issues for a thousand users is the unwanted or loud signals in sound files, it is impossible to eliminate or minimize these noise signals without knowledge their types and ranges. To overcome this issue, present an Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter (EIS-SNR-VBRAF) is proposed. Initially, the data is collected from Adaptive Myriad Filter using Time-Varying Noise and Signal-Dependent Parameters dataset. Then, the data is given for Pre-processing section, Variational Bayesian based Robust Adaptive Filter (VBRAF) is used to reduce the signal noise from Gaussian Noise, Traffic noise and Audio signal back ground noise. The proposed technique is implemented and efficacy of EIS-SNR-VBRAF technique is assessed by support of numerous performances such as BER, Signal-to-Noise-Ratio (SNR), Mean Squared Error, Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error, Structural Similarity Index, Cross-Correlation and Computational Complexity is analyzed. The proposed EIS-SNR-VBRAF method attains 21.18%, 23.52% and 23.65% lower RMSE, 21.52%, 21.76% and 23.24% higher PSNR, 21.19%, 21.73% and 20.48% higher Structural Similarity Index are compared with existing methods like early detection of mechanical malfunctions in vehicles utilizing sound signal processing (ED-MMV-SSP),noise reduction in infrasound signals based on mask coefficient binary weighting generalized cross correlation non-negative matrix factorization algorithm (NRID-MCWG-NMFA) and Click-event sound detection in automotive industry using machine/deep learning (CE-SDSI-DL) respectively.
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Ananya, M. H. "Optimized Convolutional Neural Network for High Quality Image Super Resolution." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45834.
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Abstract---- - One of the most crucial methods for digital image processing is super-resolution. In this method, one sub category is Single Image Super Resolution (SISR) and other is Multi-Frame Super Resolution (MFSR). A low-resolution image shall be used by SISR to output a high-resolution image whereas in MFSR various images of the same view with slight variations in positions are considered collectively as input to produce a single high- resolution image. In our paper Peak Signal Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM), Multi Scale Structural Similarity Index Metric (MSSSIM) and Weighted Peak Signal Noise Ratio (WPSNR) are the objective metrics used to evaluate Very Deep Super Resolution Network (VDSR) and Super Resolution Convolutional Neural Network (SRCNN) models of image super resolution Keywords—Image Super-Resolution, Convolutional Neural Network, Lightweight Networks, Deep Learning, LESRCNN, Structural Similarity Index Metric, Peak Signal Noise Ratio, Multi Scale Structural Similarity Index Metric and Weighted Peak Signal Noise Ratio
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Saxena, Parul, and Vinay Saxena. "COMPARATIVE STUDY OF WHITE GAUSSIAN NOISE REDUCTION FOR DIFFERENT SIGNALS USING WAVELET." International Journal of Research -GRANTHAALAYAH 10, no. 7 (2022): 112–23. http://dx.doi.org/10.29121/granthaalayah.v10.i7.2022.4711.
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The present work is an attempt to make a comparative study of the wavelet-based noise reduction algorithm. The algorithm has been developed and implemented in MATLAB GUI and then analyzed for different types of wavelets such as Coiflet, Daubechies, Symlet, and Biorthogonal with their different versions. This algorithm has been verified for different types of input signals from different domains. Various statistical aspects like Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal to Noise Ratio (SNR), and Peak Signal to Noise Ratio (PSNR) are analyzed for this algorithm. It is observed that the developed algorithm for noise reduction using wavelet works very well for different types of wavelets.
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Suguna, GC. "Denoising wrist pulse signals using variance thresholding technique." Indian Journal of Science and Technology 13, no. 40 (2020): 4275–86. http://dx.doi.org/10.17485/ijst/v13i40.1625.
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Background/Objectives: Denoising of the wrist pulse is a significant preprocessing stage for accurate investigation of the disease. The objective is to improve and analyze performance metrics of denoising techniques. Methods/Statistical analysis: Denoising of wrist pulse with the evaluation parameters such as PSNR, SNR, AE and RMSE has been implemented using wavelets such as Daubechies, Symlet and Biorthogonal. The performance of wavelets depends on the choice of decomposition level N and thresholding techniques. Findings: Variance thresholding technique showed significant improvement in Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR) and reduction in Absolute Error (AE) and Root Mean Square Error (RMSE) compared to other thresholding methods. Novelty/Applications: Experimental results showed drastic improvement in PSNR and SNR retaining the pathophysiological information of the wrist pulse signal for future analysis.
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R, Gayathri. "EFFICIENT NON-LOCAL AVERAGING ALGORITHM FOR MEDICAL IMAGES FOR IMPROVED VISUAL QUALITY." ICTACT Journal on Image and Video Processing 11, no. 2 (2020): 2306–9. https://doi.org/10.21917/ijivp.2020.0327.
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Image can be distorted by various ways including sensor inadequacy, transmission error, different noise factors and motion blurring. For controlling and maintaining the visual quality level of the image to be very high, it is very important to improve the image acquisition, image storage and image transmission, etc. Achieving high Peak Signal to Noise Ratio (PSNR) is essential goal of image restoration. This involves removing noises present in the image. Non-Local Means algorithm combined with Laplacian of Gaussian filter finds better results and produces good PSNR against impulse noise as well as Gaussian noise. Generally the effect of noise can be reduced using smooth filters for better results. Here, Laplacian of Gaussian (LoG) filter is applied for categorizing the edge and noisy pixels. Before that it is mandatory to obtain local smoothing of pixels. Finally the system performance is improved by averaging the non-local parameters. This is applicable to medical images also for removing impulse noise as well as Gaussian noise. The algorithm has been tested with MRI images and CT images efficiently. Better results are obtained in comparison with the previous methods with respect to better visual quality, PSNR and SSIM.
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Noor Azam, Muhammad Harith, Farida Ridzuan, and M. Norazizi Sham Mohd Sayuti. "A New Method to Estimate Peak Signal to Noise Ratio for Least Significant Bit Modification Audio Steganography." Pertanika Journal of Science and Technology 30, no. 1 (2022): 497–511. http://dx.doi.org/10.47836/pjst.30.1.27.
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Audio steganography is implemented based on three main features: capacity, robustness, and imperceptibility, but simultaneously implementing them is still a challenge. Embedding data at the Least Significant Bit (LSB) of the audio sample is one of the most implemented audio steganography methods because the method will give high capacity and imperceptibility. However, LSB has the lowest robustness among all common methods in audio steganography. To cater to this problem, researchers increased the depth of the embedding level from fourth to sixth and eighth LSB level to improve its robustness feature. However, consequently, the imperceptibility feature, which is commonly measured by Peak Signal to Noise Ratio (PSNR), is reduced due to the trade-off between imperceptibility and robustness. Currently, the lack of study on the estimation of the PSNR for audio steganography has caused the early assessment of the imperceptibility-robustness trade-off difficult. Therefore, a method to estimate PSNR, known as PSNR Estimator (PE), is introduced to enable early evaluation of imperceptibility feature for each stego-file produced by the audio steganography, which is important for the utilisation of embedding. The proposed PE estimates the PSNR based on the pattern collected from the embedment at different levels. From the evaluation, the proposed method has 99.9% of accuracy in estimating PSNR values at different levels. In comparison with the Mazdak Method, the proposed method performs better in all situations. In conclusion, the proposed PE can be used as a reference for embedding and further reducing the calculation complexity in finding the feasible value to minimise the trade-off between robustness and imperceptibility.
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GC, Suguna, and Veerabhadrappa ST. "Denoising wrist pulse signals using variance thresholding technique." Indian Journal of Science and Technology 13, no. 40 (2020): 4275–86. https://doi.org/10.17485/IJST/v13i40.1625.
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Abstract <strong>Background/Objectives:</strong> Denoising of the wrist pulse is a significant preprocessing stage for accurate investigation of the disease. The objective is to improve and analyze performance metrics of denoising techniques. <strong>Methods/Statistical analysis:</strong> Denoising of wrist pulse with the evaluation parameters such as PSNR, SNR, AE and RMSE has been implemented using wavelets such as Daubechies, Symlet and Biorthogonal. The performance of wavelets depends on the choice of decomposition level N and thresholding techniques. <strong>Findings</strong>: Variance thresholding technique showed significant improvement in Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR) and reduction in Absolute Error (AE) and Root Mean Square Error (RMSE) compared to other thresholding methods. <strong>Novelty/Applications:</strong> Experimental results showed drastic improvement in PSNR and SNR retaining the pathophysiological information of the wrist pulse signal for future analysis. <strong>Keywords</strong>: Wrist pulse; SNR; PSNR; AE; RMSE; wavelets
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Salah, Zaher, Waleed T. Al-Sit, Kamal Salah, and Esraa Elsoud. "Spatial domain noise removal filtering for low-resolution digital images." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 1627–42. https://doi.org/10.11591/ijeecs.v34.i3.pp1627-1642.
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In this research work, six different filters are applied on a low resolution 8 b/pixel gray-scale images, which operate on small sub-images (windows of 3×3 to 11×11 pixels). The enhanced images are used to compare the efficiency of the different six filters using the peak signal to noise ratio (PSNR) image quality measure. Noise peak elimination filter (PSNR)=36.63) outperforms others, such as median filter (PSNR=36.61), while corruption estimation (PSNR=36.03) significantly cuts processing time by only processing the corrupted pixels while maintaining image details. Mean filter (PSNR=34.05) is sensitive to outliers, which cause the image's sharpness and fine features to be lost. By avoiding averaging across edges, bimodal-averaging filter (PSNR=35.30), which improves on the mean filter, chooses the mean of the biggest population. The median-mean filtering (PSNR=36.32), which combines median and mean filters and determines the output pixel by averaging the median and some nearby pixels, is another improvement above averaging.
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Padcharoen, Anantachai, and Duangkamon Kitkuan. "A Mathematical Algorithm for Improving the Medical Image." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 21 (April 25, 2024): 192–99. http://dx.doi.org/10.37394/23208.2024.21.20.
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In this paper, we present a hybrid model based on total generalized variation (TGV) and shearlet with non-quadratic fidelity data terms for blurred images corrupted by impulsive and Poisson noises. Numerical experiments demonstrate that the proposed can reduce the staircase effect while preserving edges and outperform classical TV-based models in the peak signal-to-noise ratio (PSNR).
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Lee, Hwunjae, and Junhaeng Lee. "Peak Signal-to-Noise Ratio Evaluation of Server Display Monitors and Client Display Monitors in a Digital Subtraction Angiography Devices." ScholarGen Publishers 3, no. 1 (2020): 33–41. http://dx.doi.org/10.31916/sjmi2020-01-04.
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This study evaluated PSNR of server display monitor and client display monitor of DSA system. The signal is acquired and imaged during the surgery and stored in the PACS server. After that, distortion of the original signal is an important problem in the process of observation on the client monitor. There are many problems such as noise generated during compression and image storage/transmission in PACS, information loss during image storage and transmission, and deterioration in image quality when outputting medical images from a monitor. The equipment used for the experiment in this study was P's DSA. We used two types of monitors in our experiment, one is P’s company resolution 1280×1024 pixel monitor, and the other is W’s company resolution 1536×2048 pixel monitor. The PACS Program used MARO-view, and for the experiment, a PSNR measurement program using Visual C++ was implemented and used for the experiment. As a result of the experiment, the PSNR value of the kidney angiography image was 26.958dB, the PSNR value of the lung angiography image was 28.9174 dB, the PSNR value of the heart angiography image was 22.8315dB, and the PSNR value of the neck angiography image was 37.0319 dB, and the knee blood vessels image showed a PSNR value of 43.2052 dB, respectively. In conclusion, it can be seen that there is almost no signal distortion in the process of acquiring, storing, and transmitting images in PACS. However, it suggests that the image signal may be distorted depending on the resolution and performance of each monitor. Therefore, it will be necessary to evaluate the performance of the monitor and to maintain the performance.
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Youssif, Mohamed Ibrahim, Amr ElSayed Emam, and Mohamed Abd ElGhany. "Image multiplexing using residue number system coding over MIMO-OFDM communication system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4815. http://dx.doi.org/10.11591/ijece.v9i6.pp4815-4825.
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<p>Image transmission over Orthogonal Frequency-Division Multiplexing (OFDM) communication system is prone to distortion and noise due to the encountered High-Peak-to-Average-Power-Ratio (PAPR) generated from the OFDM block. This paper studies the utilization of Residue Number System (RNS) as a coding scheme for digital image transmission over Multiple-Input-Multiple-Output (MIMO) – OFDM transceiver communication system. The use of the independent parallel feature of RNS, as well as the reduced signal amplitude to convert the input signal to parallel smaller residue signals, enable to reduce the signal PAPR, decreasing the signal distortion and the Bit Error Rate (BER). Consequently, improving the received Signal-to-Noise Ratio (SNR) and enhancing the received image quality. The performance analyzed though BER, and PAPR. Moreover, image quality measurement is achieved through evaluating the Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and the correlation values between the initial and retrieved images. Simulation results had shown the performance of transmission/reception model with and without RNS coding implementation.</p><p> </p>
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M., I. Youssef, E. Emam A., and Abd Elghany M. "Image multiplexing using residue number system coding over MIMO-OFDM communication system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4815–25. https://doi.org/10.11591/ijece.v9i6.pp4815-4825.
Full textAbstract:
Image transmission over Orthogonal Frequency-Division Multiplexing (OFDM) communication system is prone to distortion and noise due to the encountered High-Peak-to-Average-Power-Ratio (PAPR) generated from the OFDM block. This paper studies the utilization of Residue Number System (RNS) as a coding scheme for digital image transmission over Multiple Input Multiple Output (MIMO) – OFDM transceiver communication system. The use of the independent parallel feature of RNS, as well as the reduced signal amplitude to convert the input signal to parallel smaller residue signals, enable to reduce the signal PAPR, decreasing the signal distortion and the Bit Error Rate (BER). Consequently, improving the received Signal-to-Noise Ratio (SNR) and enhancing the received image quality. The performance analyzed though BER, and PAPR. Moreover, image quality measurement is achieved through evaluating the Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and the correlation values between the initial and retrieved images. Simulation results had shown the performance of transmission/reception model with and without RNS coding implementation.
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Prasad, K. D., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Techniques for Improving PSNR of Liver Image by Median Filtering over Wiener Filtering." CARDIOMETRY, no. 25 (February 14, 2023): 996–1002. http://dx.doi.org/10.18137/cardiometry.2022.25.9961002.
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Aim: The purpose of this study is to use median filters and wiener filters to minimize noise in liver images in order to improve them. In addition, the output of both filters was analyzed based on their Peak Signal to Noise Ratio (PSNR). Materials and Methods: The research includes two groups; each group has a sample size of 20. Grayscale medical images collected from the kaggle website were used in this research. Samples were considered as (N=20) for guided filter and (N=20) for fast bilateral filter with total sample size 40 calculated using clinicalc.com. Image enhancement is used to enhance the niceness of a picture for the visible notion of human beings. The kaggle website was used to collect data for this study. According to clinical.com, samples were considered as size 20 for PSNR ratio of image G power of 80%, and total sample size determined. Using matlab programming and a standard data set, the Linear filtering, Median filtering were computed. Results: According to Matlab simulation results, unique median filters have a PSNR of 48.1240, while wiener filters have a PSNR of 67.8360. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical insignificant difference between both techniques. The significant value of PSNR (Peak Signal to Noise Ratio) (0.409) and p>0.05 was found in the statistical analysis. Conclusion: On ultrasound liver pictures, the innovative median filter gives greater PSNR than the wiener filter for medical image enhancing purposes, according to this study.
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Hardiansyah, Bagus, and Elvianto Dwi Hartono. "Enhanced Face Image Super-Resolution Using Generative Adversarial Network." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 10, no. 1 (2022): 31–40. http://dx.doi.org/10.33558/piksel.v10i1.4158.
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We proposed an Enhanced Face Image Generative Adversarial Network (EFGAN). Single image super-resolution (SISR) using a convolutional is often a problem in enhancing more refined texture upscaling factors. Our approach focused on mean square error (MSE), validation peak-signal-to-noise ratio (PSNR), and Structural Similarity Index (SSIM). However, the peak-signal-to-noise ratio has a high value to detail. The generative Adversarial Network (GAN) loss function optimizes the super-resolution (SR) model. Thus, the generator network is developed with skip connection architecture to improve performance feature distribution.
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Suriyan, Kannadhasan, Nagarajan Ramaingam, Sudarmani Rajagopal, Jeevitha Sakkarai, Balakumar Asokan, and Manjunathan Alagarsamy. "Performance analysis of peak signal-to-noise ratio and multipath source routing using different denoising method." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 286–92. http://dx.doi.org/10.11591/eei.v11i1.3332.
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The problem of denoising iris pictures for iris identification systems will be discussed, as well as a novel solution based on wavelet and median filters. Different salt and pepper extraction algorithms, as well as Gaussian and speckle noises, were used. Because diverse sounds decrease picture quality during image collection, noise reduction is even more important. To reduce sounds like salt and pepper, Gaussian, and speckle, filtering (median, wiener, bilateral, and Gaussian) and wavelet transform are utilised. Provide better results as compared to other ways. A study of several efficiency indicators such as peak signal-to-noise ratio (PSNR) and mean squared error will be used to demonstrate the superiority of the proposed technique (MSE).
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Kannadhasan, Suriyan, Ramaingam Nagarajan, Rajagopal Sudarmani, Sakkarai Jeevitha, Asokan Balakumar, and Alagarsamy Manjunathan. "Performance analysis of peak signal-to-noise ratio and multipath source routing using different denoising method." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 286–92. https://doi.org/10.11591/eei.v11i1.3332.
Full textAbstract:
The problem of denoising iris pictures for iris identification systems will be discussed, as well as a novel solution based on wavelet and median filters. Different salt and pepper extraction algorithms, as well as Gaussian and speckle noises, were used. Because diverse sounds decrease picture quality during image collection, noise reduction is even more important. To reduce sounds like salt and pepper, Gaussian, and speckle, filtering (median, wiener, bilateral, and Gaussian) and wavelet transform are utilised. Provide better results as compared to other ways. A study of several efficiency indicators such as peak signal-to-noise ratio (PSNR) and mean squared error will be used to demonstrate the superiority of the proposed technique (MSE).
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Purwanti Ningrum, Ika, Agfianto Eko Putra, and Dian Nursantika. "Penapisan Derau Gaussian, Speckle dan Salt&Pepper Pada Citra Warna." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 5, no. 3 (2011): 29. http://dx.doi.org/10.22146/ijccs.5209.
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Quality of digital image can decrease becouse some noises. Noise can come from lower quality of image recorder, disturb when transmission data process and weather. Noise filtering can make image better becouse will filtering that noise from the image and can improve quality of digital image. This research have aim to improve color image quality with filtering noise. Noise (Gaussian, Speckle, Salt&Pepper) will apply to original image, noise from image will filtering use Bilateral Filter method, Median Filter method and Average Filter method so can improve color image quality. To know how well this research do, we use PSNR (Peak Signal to Noise Ratio) criteria with compared original image and filtering image (image after using noise and filtering noise).This research result with noise filtering Gaussian (variance = 0.5), highest PSNR value found in the Bilateral Filter method is 27.69. Noise filtering Speckle (variance = 0.5), highest PSNR value found in the Average Filter method is 34.12. Noise filtering Salt&Pepper (variance = 0.5), highest PSNR value found in the Median Filter method is 31.27. Keywords— Bilateral Filter, image restoration, derau Gaussian, Speckle dan Salt&Pepper
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Tyukhtyaev, Dmitry. "Researching Video Conference Services on IEEE 802.11x Wireless Networks." NBI Technologies, no. 4 (December 2021): 13–18. http://dx.doi.org/10.15688/nbit.jvolsu.2021.4.2.
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The purpose of the study was to determine the dependence of the quality of video conferencing services on the characteristics of wireless communication channels and the number of users in a given network. The characteristics of the signal strength in a wireless network, measured in decibels (dB) were described in this article. The article discusses subjective and objective methods for assessing video. The PSNR and VQM metrics and the MSU Video Quality Measurement Tool software, created by the computer graphics laboratory of the Moscow State University, were used as an objective method for assessing video. For the subjective method, the DSCQS method was used. The PSNR (peak signal to noise ratio) metric is one of the most commonly used metrics. PSNR measures the peak signal-to-noise ratio between the original signal and the signal at the output of the system. PSNR does not measure all video-specific parameters, as the fidelity of the image is constantly changing depending on the visual complexity of the image, the available bit rate and even the compression method. The Video Quality Measurement (VQM) metric is described in Recommendation ITU-R BT.1683. The test results show that VQM has a high correlation with subjective methods for assessing video quality and claims to become the standard in the field of objective quality assessment.
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Ma, Shaowen. "Comparison of image compression techniques using Huffman and Lempel-Ziv-Welch algorithms." Applied and Computational Engineering 5, no. 1 (2023): 793–801. http://dx.doi.org/10.54254/2755-2721/5/20230705.
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Image compression technology is very popular in the field of image analysis because the compressed image is convenient for storage and transmission. In this paper, the Huffman algorithm and Lempel-Ziv-Welch (LZW) algorithm are introduced. They are widely used in the field of image compression, and the compressed image results of the two algorithms are calculated and compared. Based on the four dimensions of Compression Ratio (CR), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), the applicable conditions of the two algorithms in compressing small image files are analysed. The results illustrated that when the source image files are less than 300kb, the Compression Ratio (CR) of Huffman algorithm was better than that of LZW algorithm. However, for Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), which are used to represent the compressed images qualities, LZW algorithm gave more satisfactory results.
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Salau, Ayodeji Olalekan, Shruti Jain, and Joy Nnenna Eneh. "A review of various image fusion types and transforms." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1515. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1515-1522.
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Utilizing multiple views of an image is an important approach in digital photography, video editing, and medical image fusion applications. Image fusion (ImF) methods are used to improve an image's quality and remove noise from the image signal, resulting in a higher signal-to-noise ratio. A complete assessment of the literature on the different transform kinds, techniques, and rules utilized in ImF is presented in this paper. To assess the outcomes, a white flower image was fused using discrete wavelet transform (DWT) and discrete cosine transform (DCT) techniques. For validation of results, the red, green, blue (RGB) and intensity hue saturation (IHS) values of individual and fused images were evaluated. The results obtained from the fused images with the spatial IHS transform method give a remarkable performance. Furthermore, the results of the performance evaluation using DWT and DCT fusion techniques show that the same peak signal to noise ratio (PSNR) of 114.04 was achieved for both PSNR 1 and PSNR 2 for DCT, and different results were obtained for DWT. For signal to noise ratio (SNR), SNR 1 and SNR 2 achieved slightly similar values of 114.00 and 114.01 for DCT, while a SNR of 113.28 and 112.26 was achieved for SNR 1 and SNR 2 respectively.
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Salau, Ayodeji Olalekan, Shruti Jain, and Joy Nnenna Eneh. "A review of various image fusion types and transforms." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1515–22. https://doi.org/10.11591/ijeecs.v24.i3.pp1515-1522.
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Utilizing multiple views of an image is an important approach in digital photography, video editing, and medical image fusion applications. Image fusion (ImF) methods are used to improve an image's quality and remove noise from the image signal, resulting in a higher signal-to-noise ratio. A complete assessment of the literature on the different transform kinds, techniques, and rules utilized in ImF is presented in this paper. To assess the outcomes, a white flower image was fused using discrete wavelet transform (DWT) and discrete cosine transform (DCT) techniques. For validation of results, the red, green, blue (RGB) and intensity hue saturation (IHS) values of individual and fused images were evaluated. The results obtained from the fused images with the spatial IHS transform method give a remarkable performance. Furthermore, the results of the performance evaluation using DWT and DCT fusion techniques show that the same peak signal to noise ratio (PSNR) of 114.04 was achieved for both PSNR 1 and PSNR 2 for DCT, and different results were obtained for DWT. For signal to noise ratio (SNR), SNR 1 and SNR 2 achieved slightly similar values of 114.00 and 114.01 for DCT, while a SNR of 113.28 and 112.26 was achieved for SNR 1 and SNR 2 respectively.
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Tjahjadi, Jhonatan, Padmavati Tanuwijaya, and Yosefina Finsensia Riti. "Analisis Perbandingan Algoritme Penghapusan Noise pada Citra X-Ray Paru - Paru." Pseudocode 10, no. 2 (2023): 80–89. http://dx.doi.org/10.33369/pseudocode.10.2.80-89.
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Pulmonary X-ray is a medical diagnostic method used to produce internal lung images. However, the X-ray process is often interrupted when capturing images, resulting in noisy image results. This condition diminishes the clarity of information contained in the lung X-ray images. Therefore, noise removal or denoising is essential. Denoising is a fundamental image processing technique aimed at improving image quality for optimal information transmission. This study applies denoising methods to 20 datasets of pulmonary X-ray images using Median, Mean, Gaussian, Bilateral, and Wiener filters, with Python and the OpenCV Library. Error measurement for noise filtering is conducted using Peak Signal-to-Noise Ratio and Mean Square Error methods. The research results show that the median filter stands out as an excellent denoising method, outperforming others with a Peak Signal-to-Noise Ratio of 37.6444 and a Mean Square Error of 11.3339 for Salt and Pepper Noise. Keywords: Denoising; Filtering; MSE; PSNR; X-Ray.
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Salah, Zaher, Waleed T. Al-Sit, Kamal Salah, and Esraa Elsoud. "Spatial domain noise removal filtering for low-resolution digital images." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 1627. http://dx.doi.org/10.11591/ijeecs.v34.i3.pp1627-1642.
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<p>n this research work, six different filters are applied on a low resolution 8 b/pixel gray-scale images, which operate on small sub-images (windows of 3×3 to 11×11 pixels). The enhanced images are used to compare the efficiency of the different six filters using the peak signal to noise ratio (PSNR) image quality measure. Noise peak elimination filter (PSNR)=36.63) outperforms others, such as median filter (PSNR=36.61), while corruption estimation (PSNR=36.03) significantly cuts processing time by only processing the corrupted pixels while maintaining image details. Mean filter (PSNR=34.05) is sensitive to outliers, which cause the image's sharpness and fine features to be lost. By avoiding averaging across edges, bimodal-averaging filter (PSNR=35.30), which improves on the mean filter, chooses the mean of the biggest population. The median-mean filtering (PSNR=36.32), which combines median and mean filters and determines the output pixel by averaging the median and some nearby pixels, is another improvement above averaging.</p>
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Prasad, K. D., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Techniques for Improving PSNR of Liver Images by Linear Contrast Algorithm over Median Filtering." CARDIOMETRY, no. 25 (February 14, 2023): 983–89. http://dx.doi.org/10.18137/cardiometry.2022.25.983989.
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Aim: The goal of this research is to reduce noise present in the liver images in order to enhance it using linear contrast and median filters. And also to analyze output of both the filters based on its Peak Signal to Noise Ratio (PSNR). Materials and Methods: The research includes two groups; each group has a sample size of 20. Grayscale medical images collected from the kaggle website were used in this research. Samples were considered as (N=20) for guided filter and (N=20) for fast bilateral filter with total sample size 40 calculated using clinicalc.com. For this study, the affected and normal liver images were collected from the Kaggle website. Then the linear filtering, median filtering algorithms were executed using Matlab software. Sample size was calculated using clinicalc.com, and the comparison analysis has been carried out through SPSS software. This research contains two groups, with a Gpower of 80 percent. The performance of the novel median filter is evaluated and the performance measure PSNR is compared with the linear contrast filter. Result: Based on Matlab simulation results, the PSNR of novel median filters is 76.0355 and linear contrast filters have PSNR of 57.1785. From the statistical analysis, it is observed that the significant value of PSNR (Peak Signal to Noise Ratio) (0.409) and p>0.05. Conclusion: This study reveals that for the medical image enhancement purpose the novel median filter provides better PSNR than the linear contrast algorithm on ultrasound liver images.
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Chen, Yunfei, Kaimin Yu, Minfeng Wu та ін. "Wavelet Decomposition Layer Selection for the φ-OTDR Signal". Photonics 11, № 2 (2024): 137. http://dx.doi.org/10.3390/photonics11020137.
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The choice of wavelet decomposition layer (DL) not only affects the denoising quality of wavelet denoising (WD), but also limits the denoising efficiency, especially when dealing with real phase-sensitive optical time-domain reflectometry (φ-OTDR) signals with complex signal characteristics and different noise distributions. In this paper, a straightforward adaptive DL selection method is introduced, which dose not require known noise and clean signals, but relies on the similarity between the probability density function (PDF) of method noise (MN) and the PDF of Gaussian white noise. Validation is carried out using hypothetical noise signals and measured φ-OTDR vibration signals by comparison with conventional metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The proposed wavelet DL selection method contributes to the fast processing of distributed fiber optic sensing signals and further improves the system performance.
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Yousuf, M. A., and M. N. Nobi. "A New Method to Remove Noise in Magnetic Resonance and Ultrasound Images." Journal of Scientific Research 3, no. 1 (2010): 81. http://dx.doi.org/10.3329/jsr.v3i1.5544.
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In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images. In the proposed method median filter is modified by adding more features. Experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE). Experimental results of magnetic resonance (MR) image and ultrasound image demonstrate that the proposed algorithm is comparable to popular image smoothing algorithms.Key words: Magnetic resonance image; Ultrasound image; PSNR; SNR; RMSE.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5544 J. Sci. Res. 3 (1), 81-89 (2011)
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C. Jenifer Kamalin, Dr. G. Muthu Lakshmi. "Comparative Analysis on Image Compression Algorithm Based on Block Truncation Coding." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 2623–30. http://dx.doi.org/10.52783/tjjpt.v44.i4.1328.
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Block Truncation Coding is a quick and effective method for compressing images, because it employs a non-parametric one-bit or two-bit quantization procedure that adapts to the features of the image. This paper describes and compares the performance of BTC and BTC based techniques such as Enhanced Block Truncation Coding (EBTC), Modified Block Truncation Coding (MBTC), and Adaptive Block Truncation Coding – Edge Quantization (ABTC – EQ). These techniques were applied on the general images and their performance were measured using the metrics such as Peak Signal Noise Ratio (PSNR), Structured Similarity Index Measure (SSIM), Weighted Peak Signal Noise Ratio (WPSNR), Feature Similarity Index Measure (FSIM) and Compression Ratio (CR).
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Shahbakhti, Mohammad, Maxime Maugeon, Matin Beiramvand, and Vaidotas Marozas. "Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG." Brain Sciences 9, no. 12 (2019): 352. http://dx.doi.org/10.3390/brainsci9120352.
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The electroencephalogram signal (EEG) often suffers from various artifacts and noises that have physiological and non-physiological origins. Among these artifacts, eye blink, due to its amplitude is considered to have the most influence on EEG analysis. In this paper, a low complexity approach based on Stationary Wavelet Transform (SWT) and skewness is proposed to remove eye blink artifacts from EEG signals. The proposed method is compared against Automatic Wavelet Independent Components Analysis (AWICA) and Enhanced AWICA. Normalized Root Mean Square Error (NRMSE), Peak Signal-to-Noise Ratio (PSNR), and correlation coefficient ( ρ ) between filtered and pure EEG signals are utilized to quantify artifact removal performance. The proposed approach shows smaller NRMSE, larger PSNR, and larger correlation coefficient values compared to the other methods. Furthermore, the speed of execution of the proposed method is considerably faster than other methods, which makes it more suitable for real-time processing.
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Qin, Feng Qing, Li Hong Zhu, Li Lan Cao, and Wa Nan Yang. "Gaussian Noised Single-Image Super Resolution Reconstruction." Applied Mechanics and Materials 457-458 (October 2013): 1032–36. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1032.
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A framework is proposed to reconstruct a super resolution image from a single low resolution image with Gaussian noise. The degrading processes of Gaussian blur, down-sampling, and Gaussian noise are all considered. For the low resolution image, the Gaussian noise is reduced through Wiener filtering algorithm. For the de-noised low resolution image, iterative back projection algorithm is used to reconstruct a super resolution image. Experiments show that de-noising plays an important part in single-image super resolution reconstruction. In the super reconstructed image, the Gaussian noise is reduced effectively and the peak signal to noise ratio (PSNR) is increased.
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Qin, Feng Qing, Li Hong Zhu, Li Lan Cao, and Wa Nan Yang. "Single-Image Super Resolution Reconstruction with Pepper and Salt Noise." Applied Mechanics and Materials 427-429 (September 2013): 1817–21. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1817.
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In order to improve the resolution of single image with Pepper and Salt noise, a framework is proposed. In the low resolution imaging model, the Gaussian blur, down-sampling, as well as Pepper and Salt noise are considered. For the low resolution image, the Pepper and Salt noise is reduced through median filtering method. Super resolution reconstruction is performed on the de-noised low resolution image by iterative back projection algorithm. Experimental results show that the Pepper and Salt noise are removed effectively and the peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved.
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Wisnu, Widiarto, Hariadi Mochamad, and Mulyanto Yuniarno Eko. "Keyframe Selection of Frame Similarity to Generate Scene Segmentation Based on Point Operation." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2839–46. https://doi.org/10.11591/ijece.v8i5.pp2839-2846.
Full textAbstract:
Video segmentation has been done by grouping similar frames according to the threshold. Two-frame similarity calculations have been performed based on several operations on the frame: point operation, spatial operation, geometric operation and arithmatic operation. In this research, similarity calculations have been applied using point operation: frame difference, gamma correction and peak signal to noise ratio. Three-point operation has been performed in accordance with the intensity and pixel frame values. Frame differences have been operated based on the pixel value level. Gamma correction has analyzed pixel values and lighting values. The peak signal to noise ratio (PSNR) has been related to the difference value (noise) between the original frame and the next frame. If the distance difference between the two frames was smaller then the two frames were more similar. If two frames had a higher gamma correction factor, then the correction factor would have an increasingly similar effect on the two frames. If the value of PSNR was greater then the comparison of two frames would be more similar. The combination of the three point operation methods would be able to determine several similar frames incorporated in the same segment.
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Prathama, Wayan Adhitya, and I. Gede Arta Wibawa. "Noise Qualification in Bali Palm Leaf Image with Gaussian Filter Method." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 3 (2020): 301. http://dx.doi.org/10.24843/jlk.2020.v08.i03.p12.
Full textAbstract:
In improving the quality of the image basically makes the appearance of an image better than before. One thing that is done in improving the image quality is noise qualification. This noise qualification aims to reduce the level of noise contained in a digital image. In this study, the image used is the image of Bali palm leaf. There are many methods that can be used to qualify for noise. One of them is the Gaussian filter. In this study, Gaussian Filter is used as a method to qualify the noise contained in the palm leaf image. The image quality after the noise qualification process is calculated using PSNR (Peak Signal to Noise Ratio). The higher the PSNR value obtained, the better the image quality. In this study, the PSNR value obtained in the palm leaf image after processing the noise qualification is 54.7625 db.
 Keywords: Gaussian Filter, Noise, PSNR, Bali Palm Leaf, Image
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Franca Oyiwoja Okoh and John Actor Ocheje. "Evaluating denoising performances of basic filters in the detection of microcalcifications on mammogram images." International Journal of Science and Research Archive 9, no. 2 (2023): 201–10. http://dx.doi.org/10.30574/ijsra.2023.9.2.0544.
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This research evaluates the denoising abilities of some image-processing filters used in facilitating the early detection of microcalcifications in breast tissues. The mean, median and Gaussian filters were employed to denoise mammogram images of microcalcification breast phantoms of various densities. The performances of the filters were assessed by evaluating the mean squared error (MSE), peak signal-to-noise ratio (PSNR), and signal-to-noise ratio (SNR). All experiments were carried out on MATLAB R2020a platform. The results revealed that the Gaussian filter recorded optimal performance in denoising images with all 3 types of added noises compared to the mean and median filters. The PSNR value of the heterogeneous phantom (PVAL/H) was superior to those of the less dense (PVAL/E), dense (PVAL), and extremely dense (PVAL/G) phantoms for all the tested filters. The results of this work agree with the high contrast recorded by the original image of PVAL/H.
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Li, Yucheng, Yang Zhang, Deyu Jia, Song Gao, and Muqun Zhang. "Noise Impact Analysis in Computer-Generated Holography Based on Dual Metrics Evaluation via Peak Signal-to-Noise Ratio and Structural Similarity Index Measure." Applied Sciences 15, no. 11 (2025): 6047. https://doi.org/10.3390/app15116047.
Full textAbstract:
This study investigates the noise impact on reconstructed images in computer-generated holography (CGH) through theoretical analysis and Matlab 2015b simulations. By quantitatively injecting noise to mimic practical interference environments, we systematically analyze the degradation mechanisms of four CGH types: detour-phase, modified off-axis beam reference, kinoform, and interference type. A dual-metric evaluation framework combining peak signal-to-noise ratio (PSNR) and the Structural Similarity Index Measure (SSIM) is proposed. Results demonstrate that increasing noise intensity induces progressive declines in reconstruction quality, manifested as PSNR reduction and SSIM-based structural fidelity loss. The findings provide theoretical guidance for noise suppression, parameter optimization, and algorithm selection in CGH systems, advancing its applications in optical encryption and high-precision imaging.