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Journal articles on the topic 'Denoising Filters'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Dai, Ming Hong. "The Development of Wavelet Transform and its Application in Image Denoise." Advanced Materials Research 694-697 (May 2013): 2003–8. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2003.

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The paper introduces Laplace pyramid, Ridgelet and Curvelet principle, structure and methods, and their denoising experimental studies. It also introduces the traditional direction filter of principle, structure and methodology, and the simulation experiments show that its image denoising PSNR is slightly lower than wavelet but denoising image visual quality is better than former. To that end, proposed a new direction filters that uniform direction filter banks and non-uniform direction filters, proved filter passband condition and related design and implementation issues were discussed. nonli

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A., Afreen Habiba, and Dr.B.Raghu2. "IMAGE DENOISING IN MRI IMAGES USING CONTOURLET TRANSFORM AND COMPARISON OF FILTERING METHODS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 7 (2017): 150–60. https://doi.org/10.5281/zenodo.835762.

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In diagnosis of medical images, operations such as feature extraction and object recognition plays the key role.. These operations will become difficult if the images are corrupted with noises. Several types of noise were introduced in the images during image acquisition, transfer & storage. The main objective is to remove the noise from the input image. Image Denoising is an utmost challenge for Researchers, developing Image denoising algorithms is a difficult task, since fine details in a medical image should not be destroyed during noise removal during the diagnosis of information. Medi

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Salehi, Hadi, and Javad Vahidi. "An Ultrasound Image Despeckling Method Based on Weighted Adaptive Bilateral Filter." International Journal of Image and Graphics 20, no. 03 (2020): 2050020. http://dx.doi.org/10.1142/s0219467820500205.

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Images are widely used in engineering. Unfortunately, ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a critical preprocessing step. Therefore, a robust despeckling method and accurate evaluation of images are suggested. We suggest three phases and a three-step denoising filter. In the first phase, the coefficients of variation are computed from the noisy image. The second phase is a three-step denoising filter. The first step is denoising of extreme levels of homogeneous regions, based on fuzzy homogeneous regions. The second step is a pr

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Gallegos-Funes, F., J. Martínez-Valdés, R. Cruz-Santiago, and J. López-Bonilla. "Wavelet Order Statistics Filters for Image Denoising." Journal of Scientific Research 1, no. 2 (2009): 248–57. http://dx.doi.org/10.3329/jsr.v1i2.2311.

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This paper presents the wavelet order statistics filters for the removal of impulsive and speckle noise in color image applications. The proposed filtering scheme is defined as two filters in the wavelet domain to conform to the structure of a general filter that can be modified in some headings. The first filter is based on redundancy of approaches and the second one is the wavelet domain iterative center weighted median algorithm. With the structure of the proposed filter different implementations for the estimation of the noisy sample are carried out using different order statistics algorit

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Biradar, Nagashettappa, M. L. Dewal, and Manoj Kumar Rohit. "Edge Preserved Speckle Noise Reduction Using Integrated Fuzzy Filters." International Scholarly Research Notices 2014 (October 30, 2014): 1–11. http://dx.doi.org/10.1155/2014/876434.

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Echocardiographic images are inherent with speckle noise which makes visual reading and analysis quite difficult. The multiplicative speckle noise masks finer details, necessary for diagnosis of abnormalities. A novel speckle reduction technique based on integration of geometric, wiener, and fuzzy filters is proposed and analyzed in this paper. The denoising applications of fuzzy filters are studied and analyzed along with 26 denoising techniques. It is observed that geometric filter retains noise and, to address this issue, wiener filter is embedded into the geometric filter during iteration

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Jiang, Hai Bo, and Jing Zhi Cai. "Performance Analysis of Several Common Filter." Applied Mechanics and Materials 220-223 (November 2012): 1446–49. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1446.

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Denoising is the initial stage of image processing, in preparation for the subsequent processing of the image. This article describes a field of several denoising used filters include average filter, median filter, Wiener filter, Kalman filter. Combination of diagrams, will describe their filtering principle, at the end of this paper,analysis signal to noise ratio of image and other performance indicators .

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Snehalatha, M., Dr N. Ramamurthy, K. Swetha, K. Vishnupriya, P. Sreelekha, and N. Niharika. "An Image Denoising in Spatial Domain using Bilateral Filter." Journal of Electronics and Communication Systems 7, no. 2 (2022): 9–14. http://dx.doi.org/10.46610/joecs.2022.v07i02.002.

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The use of bilateral filters is one of the most efficient and resource-saving image processing techniques. This method does not use edge smoothing to filter the image, but it does use non-linear spatial averaging. The characteristics of the filters in the filtering process outlined above are quite important. The outputs and results are dramatically affected by even minor changes in filter parameter values. The author contributed two pieces to this publication. The author has contributed to the study of parameter selection of bilateral filters that are optimal in nature in the context of pictur

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Bui, Ngoc Ha, Tien Hung Bui, Thuy Duong Tran, Kim Tuan Tran, and Ngoc Toan Tran. "Evaluation of the effect of filters on reconstructed image quality from cone beam CT system." Nuclear Science and Technology 11, no. 1 (2021): 35–47. http://dx.doi.org/10.53747/jnst.v11i1.130.

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: 3D Filtered Back Projection (FBP) is a three-dimensional reconstruction algorithm usually used in Cone Beam Computed Tomography (CBCT) system. FBP is one of the most popular algorithms due to its reconstruction is fast while quality of the result is acceptable. It can also handle a more considerable amount of data with same computer performance with other algorithms. However, the quality of a reconstructed image by the FBP algorithm strongly depends on spatial filters and denoising filters applied to projections. In this paper an evaluation of the reconstructed image quality of the CBCT syst

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Kim, Bae-Guen, Seong-Hyeon Kang, Chan Rok Park, Hyun-Woo Jeong, and Youngjin Lee. "Noise Level and Similarity Analysis for Computed Tomographic Thoracic Image with Fast Non-Local Means Denoising Algorithm." Applied Sciences 10, no. 21 (2020): 7455. http://dx.doi.org/10.3390/app10217455.

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Although conventional denoising filters have been developed for noise reduction from digital images, these filters simultaneously cause blurring in the images. To address this problem, we proposed the fast non-local means (FNLM) denoising algorithm which would preserve the edge information of objects better than conventional denoising filters. In this study, we obtained thoracic computed tomography (CT) images from a male adult mesh (MASH) phantom modeled by computer and a five-year-old phantom to perform both the simulation study and the practical study. Subsequently, the FNLM denoising algor

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Muhammad Fathi Mohd Zain, Wan Mahani Hafizah Wan Mahmud, and Hong-Seng Gan. "Preliminary Analysis on the Effect of Different Denoising Techniques towards Texture Features of MRI Images of Alzheimer’s Disease." Journal of Advanced Research in Applied Sciences and Engineering Technology 31, no. 2 (2023): 234–44. http://dx.doi.org/10.37934/araset.31.2.234244.

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Early detection of Alzheimer’s disease (AD) has become one of the major research topics nowadays. The utilization of the computerized system may help medical experts to better understand and analyse the magnetic resonance imaging (MRI) images of AD patients for early detection. One of the commonly steps taken for the analysis of the image is image denoising using certain filters. However, finding shows that previous researchers use different approaches. This study aims to analyse the effect of different denoising techniques towards detection of Alzheimer’s disease. Data of two different groups

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Pereira, Tiago, Victor Santos, Tiago Gameiro, Carlos Viegas, and Nuno Ferreira. "Evaluation of Different Filtering Methods Devoted to Magnetometer Data Denoising." Electronics 13, no. 11 (2024): 2006. http://dx.doi.org/10.3390/electronics13112006.

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In this article, we describe a performance comparison conducted between several digital filters intended to mitigate the intrinsic noise observed in magnetometers. The considered filters were used to smooth the control signals derived from the magnetometers, which were present in an autonomous forestry machine. Three moving average FIR filters, based on rectangular Bartlett and Hanning windows, and an exponential moving average IIR filter were selected and analyzed. The trade-off between the noise reduction factor and the latency of the proposed filters was also investigated, taking into accou

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Bader, Milad, Robert G. Clapp, and Biondo Biondi. "Denoising for full-waveform inversion with expanded prediction-error filters." GEOPHYSICS 86, no. 5 (2021): V445—V457. http://dx.doi.org/10.1190/geo2020-0573.1.

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Low-frequency data of less than 5 Hz are essential to the convergence of full-waveform inversion (FWI) toward a useful solution. They help to build the velocity model low wavenumbers and reduce the risk of cycle skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio (S/N) and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often, field data are high-pass filtered before any processing step, sacrificing weak but essential signal for FWI. We have denoised the low-frequency data using prediction-error

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Rudra, Krishna. "Hybrid Signal Denoising in Seismic Applications Using Sinc Filtering and Machine Learning Regression." Journal of Emerging Technologies and Innovative Research 12, no. 3 (2025): f553—f559. https://doi.org/10.5281/zenodo.15110930.

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Accurate seismic signal analysis is vital for earthquake detection, subsurface imaging, and geotechnical assessments. However, field-acquired seismic data is often contaminated by various noise sources. Traditional low-pass filters, like sinc-based FIR filters, reduce high-frequency noise but may distort signal features. This study presents a hybrid denoising approach combining a sinc FIR filter with a machine learning (ML) enhancement using a linear regression (LinearFit) model. A clean seismic signal was simulated and degraded with additive white Gaussian noise, then sequentially processed t

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Hassan, Raaid N. "A comparison between PCA and some enhancement filters for denoising astronomical images." Iraqi Journal of Physics (IJP) 11, no. 22 (2019): 82–92. http://dx.doi.org/10.30723/ijp.v11i22.356.

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This paper includes a comparison between denoising techniques by using statistical approach, principal component analysis with local pixel grouping (PCA-LPG), this procedure is iterated second time to further improve the denoising performance, and other enhancement filters were used. Like adaptive Wiener low pass-filter to a grayscale image that has been degraded by constant power additive noise, based on statistics estimated from a local neighborhood of each pixel. Performs Median filter of the input noisy image, each output pixel contains the Median value in the M-by-N neighborhood around th

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JAYAWARDENA, ASHOKA, and PAUL KWAN. "FINITE IMPULSE RESPONSE DOUBLE DENSITY FILTER BANKS AND FRAMELETS." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 01 (2013): 1350010. http://dx.doi.org/10.1142/s0219691313500100.

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In this paper, we focus on the design of oversampled filter banks and the resulting framelets. The framelets obtained exhibit improved shift invariant properties over decimated wavelet transform. Shift invariance has applications in many areas, particularly denoising, coding and compression. Our contribution here is on filter bank completion. In addition, we propose novel factorization methods to design wavelet filters from given scaling filters.

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Zhang, Xin-Ming, Qiang Kang, Jin-Feng Cheng, and Xia Wang. "Adaptive Four-dot Median Filter for Removing 1-99% Densities of Salt-and-Pepper Noise in Images." Journal of Information Technology Research 11, no. 3 (2018): 47–61. http://dx.doi.org/10.4018/jitr.2018070104.

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In order to accelerate denoising and improve the denoising performance of the current median filters, an Adaptive Four-dot Median Filter (AFMF) for image restoration is proposed in this article. AFMF is not only very efficient and fast in logic execution, but also it can restore the corrupted images with 1–99% densities of salt-and-pepper noise to the satisfactory ones. Without any complicated operation for noise detection, it intuitively and simply distinguishes impulse noises, while keeping the noise-free pixels intact. Only the uncorrupted pixels of the four-dot mask in adaptive filtering w

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Abid Ali, Alaa Abid Muslam, Mohammed Iqbal Dohan, and Saif Khalid Musluh. "Denoising of image using bilateral filtering in multiresolution." APTIKOM Journal on Computer Science and Information Technologies 3, no. 1 (2020): 6–12. http://dx.doi.org/10.34306/csit.v3i1.76.

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One of the very efficient and resource conservative image processing methodology is with the help of bilateral filters. This technique filters the image without the help of edge smoothing but it does employs spatial averaging in a non-linear way. The filtering technique discussed above is very much dependent on the parameters of its filters. A very slight change in filter parameter values effects the outputs and results in a most drastic manner. In this paper, the author has worked on two contributions. In the applications concerning image denoising, the author has contributed in study of the

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Ali, Alaa Abid Muslam Abid, Mohammed Iqbal Dohan, and Saif Khalid Musluh. "Denoising of image using bilateral filtering in multiresolution." APTIKOM Journal on Computer Science and Information Technologies 3, no. 1 (2018): 6–12. http://dx.doi.org/10.11591/aptikom.j.csit.80.

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One of the very efficient and resource conservative image processing methodology is with the help of bilateral filters. This technique filters the image without the help of edge smoothing but it does employs spatial averaging in a non-linear way. The filtering technique discussed above is very much dependent on the parameters of its filters. A very slight change in filter parameter values effects the outputs and results in a most drastic manner. In this paper, the author has worked on two contributions. In the applications concerning image denoising, the author has contributed in study of the

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19

Virk, Amardeep Singh, Mandeep Kaur, and Lovely Passrija. "Performance Evaluation of Image Enhancement Techniques in Spatial and Wavelet Domains." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (2012): 162–66. http://dx.doi.org/10.24297/ijct.v3i1c.2771.

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Denoising is one of the important tasks in image processing. Despite the significant research conducted on this topic, the development of efficient denoising methods is still a compelling challenge. In this paper, spatial domain methods and Wavelet Domain Methods of image denoising have been evaluated. The medical ultrasound images suffer from speckle noise which is multiplicative in nature and more difficult to remove than additive noise. In the spatial filter methods Median Filter and Wiener Filter are implemented. These methods are based on the simple formulas that are proposed by different

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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

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Majeed Laftah, Muna. "Image Denoising Using Multiwavelet Transform with Different Filters and Rules." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 15 (2021): 140. http://dx.doi.org/10.3991/ijim.v15i15.24183.

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<p class="0abstract">Image denoising is a technique for removing unwanted signals called the noise, which coupling with the original signal when transmitting them; to remove the noise from the original signal, many denoising methods are used. In this paper, the Multiwavelet Transform (MWT) is used to denoise the corrupted image by Choosing the HH coefficient for processing based on two different filters Tri-State Median filter and Switching Median filter. With each filter, various rules are used, such as Normal Shrink, Sure Shrink, Visu Shrink, and Bivariate Shrink. The proposed algorith

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Wang, Jiefei, Yupeng Chen, Tao Li, Jian Lu, and Lixin Shen. "A Residual-Based Kernel Regression Method for Image Denoising." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5245948.

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We propose a residual-based method for denoising images corrupted by Gaussian noise. In the method, by combining bilateral filter and structure adaptive kernel filter together with the use of the image residuals, the noise is suppressed efficiently while the fine features, such as edges, of the images are well preserved. Our experimental results show that, in comparison with several traditional filters and state-of-the-art denoising methods, the proposed method can improve the quality of the restored images significantly.

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Rahimian, Abdurrahim, Mahanaz Etehadtavakol, Masoud Moslehi, and Eddie Y. K. Ng. "Myocardial Perfusion Single-Photon Emission Computed Tomography (SPECT) Image Denoising: A Comparative Study." Diagnostics 13, no. 4 (2023): 611. http://dx.doi.org/10.3390/diagnostics13040611.

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The present study aimed to evaluate the effectiveness of different filters in improving the quality of myocardial perfusion single-photon emission computed tomography (SPECT) images. Data were collected using the Siemens Symbia T2 dual-head SPECT/Computed tomography (CT) scanner. Our dataset included more than 900 images from 30 patients. The quality of the SPECT was evaluated after applying filters such as the Butterworth, Hamming, Gaussian, Wiener, and median–modified Wiener filters with different kernel sizes, by calculating indicators such as the signal-to-noise ratio (SNR), peak signal-to

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Sekhar, B. V. D. S., P. V. G. D. Prasad Reddy, S. Venkataramana, Vedula V. S. S. S. Chakravarthy, and P. Satish Rama Chowdary. "Image Denoising Using Novel Social Grouping Optimization Algorithm with Transform Domain Technique." International Journal of Natural Computing Research 8, no. 4 (2019): 28–40. http://dx.doi.org/10.4018/ijncr.2019100103.

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In recent days, image communication has evolved in many fields like medicine, entertainment, gaming, mail, etc. Thus, it is an immediate need to denoise the received image because noise that is added in the channel during communication alters or deteriorates information contained in the image. Any image processing techniques concerned with image denoising or image noise removal has to be started with the spatial domain and end with the transform domain. A lot of research was carried out in the spatial domain by modifying the performance of different image filters such as mean filters, median f

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Рубель, Андрей Сергеевич, та Владимир Васильевич Лукин. "ЛОКАЛЬНО-АДАПТИВНА ФІЛЬТРАЦІЯ ЗОБРАЖЕНЬ З ВИКОРИСТАННЯМ ТЕТРОЛЕТ ПЕРЕТВОРЕННЯ". Aerospace Technic and Technology, № 5 (7 грудня 2017): 92–99. http://dx.doi.org/10.32620/aktt.2017.5.13.

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Image filtering is one of the main tasks in image processing. Images are inevitably subject to noise during image formation and subsequent transmission. Thus, it is desirable to remove noise. Image denoising (filtering) improves visual appearance and facilitates subsequent automatic processing (segmentation, classification, detection of edges). A large number of filters has been developed so far. Among them, filters based on orthogonal transforms as well as non-local filters are the most effective. One of the representatives of filters based on orthogonal transforms is the standard sliding win

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Sun, Mingqian. "Exploring Denoising Applications of One-Dimensional Fourier Transform in Microphone Filters." Highlights in Science, Engineering and Technology 111 (August 19, 2024): 192–99. http://dx.doi.org/10.54097/0je7zb73.

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This paper presents a comprehensive exploration of the denoising application of one-dimensional Fourier transform in microphone filters. Beginning with an elucidation of the fundamental principles of one-dimensional Fourier transform, the study delves into its utility in denoising, encompassing short-time Fourier transform and frequency domain filtering. The analysis further scrutinizes sound signal characteristics, noise sources, and processing methodologies. Detailed discussions ensue regarding filter design methodologies grounded in one-dimensional Fourier transform. Through case analyses a

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Verma, Atul Kumar, Barjinder Singh Saini, and Taranjit Kaur. "Image Denoising using Alexander Fractional Hybrid Filter." International Journal of Image and Graphics 18, no. 01 (2018): 1850003. http://dx.doi.org/10.1142/s0219467818500031.

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In this paper, a hybrid filter based on the concept of fractional calculus and Alexander polynomial is proposed. The hybrid filtering mask is constructed by convolving the designed Alexander fractional differential and integral masks. The hybrid mask shows high robustness for images corrupted with Gaussian, salt & pepper, and speckle noises. For the experimentation, the standard and real world noisy images are used. The qualitative comparison shows that the proposed hybrid filter has better denoising with high edge preserving capability as compared to the other existing filters. Quantitati

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Abdul Wadood *, Mohammed, and Asmaa Ghalib Jaber. "Gaussian Denoising for the First Image from The James Webb Space Telescope “Carina Nebula” using Non-Linear Filters." Journal of Economics and Administrative Sciences 30, no. 143 (2024): 420–34. http://dx.doi.org/10.33095/59cx4m31.

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Noise, including Gaussian noise, distorts images during transition or acquisition process, reducing required information. Removing or reducing this noise is crucial in image processing. The James Webb Space Telescope (JWST) is a vital tool for enhancing our understanding of the universe, providing valuable scientific data and inspiring global interest. In this paper we introduce several nonlinear (Non-Local Mean, Bilateral, and classical) filters to remove the Gaussian noise from the Carina Nebula Image, the first image taken by (JWST) on 12 July 2022. These nonlinear filters were therefore se

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Kaur, Bobbinpreet, Ayush Dogra, and Bhawna Goyal. "Comparative Analysis of Bilateral Filter and its Variants for Magnetic Resonance Imaging." Open Neuroimaging Journal 13, no. 1 (2020): 21–29. http://dx.doi.org/10.2174/1874440002013010021.

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Background: With the increase in research in the direction of making images noise free a number of algorithms have been designed. Methods: The choice of Denoising method will be made in such a way that it reduces or removes noise content on one hand and on the other hand it preserves the information content of the image. Our article focuses on analyzing the performance of bilateral filters and its derivatives for Denoising of MRI images. Results: The bilateral filter is a hybridized version of basic range filtering and domain filtering techniques. Conclusion: A comparative review of these filt

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Flenner, Silja, Stefan Bruns, Elena Longo, et al. "Machine learning denoising of high-resolution X-ray nanotomography data." Journal of Synchrotron Radiation 29, no. 1 (2022): 230–38. http://dx.doi.org/10.1107/s1600577521011139.

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High-resolution X-ray nanotomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic segmentation. Filtering methods are often required for a detailed quantitative analysis. However, most filters induce blurring in the reconstructed tomograms. Here, machine learning (ML) techniques offer a powerful alternative to conventional filtering methods. In this article, we verify that a self-supervised denoising ML technique can be used in a very

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Ma, Ruijun, Shuyi Li, Bob Zhang, and Zhengming Li. "Generative Adaptive Convolutions for Real-World Noisy Image Denoising." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1935–43. http://dx.doi.org/10.1609/aaai.v36i2.20088.

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Recently, deep learning techniques are soaring and have shown dramatic improvements in real-world noisy image denoising. However, the statistics of real noise generally vary with different camera sensors and in-camera signal processing pipelines. This will induce problems of most deep denoisers for the overfitting or degrading performance due to the noise discrepancy between the training and test sets. To remedy this issue, we propose a novel flexible and adaptive denoising network, coined as FADNet. Our FADNet is equipped with a plane dynamic filter module, which generates weight filters with

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Hu, Jinrong, Yifei Pu, Xi Wu, Yi Zhang, and Jiliu Zhou. "Improved DCT-Based Nonlocal Means Filter for MR Images Denoising." Computational and Mathematical Methods in Medicine 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/232685.

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The nonlocal means (NLM) filter has been proven to be an efficient feature-preserved denoising method and can be applied to remove noise in the magnetic resonance (MR) images. To suppress noise more efficiently, we present a novel NLM filter based on the discrete cosine transform (DCT). Instead of computing similarity weights using the gray level information directly, the proposed method calculates similarity weights in the DCT subspace of neighborhood. Due to promising characteristics of DCT, such as low data correlation and high energy compaction, the proposed filter is naturally endowed wit

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Gopi, Telagamalla. "Comparison and Denoising of OCT Images with Filters." International Journal of Science and Research (IJSR) 12, no. 12 (2023): 1888–90. http://dx.doi.org/10.21275/sr231225165352.

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Rudnitskii, A. G., M. A. Rudnytska, L. V. Tkachenko, and E. D. Pechuk. "Application of fuzzy logic in finding the optimal filter in optoacoustics problems." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 1 (2021): 43–54. http://dx.doi.org/10.17721/1812-5409.2021/1.5.

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Denoising is an important step in the early stage of signal preprocessing in optoacoustic applications. The efficiency of such modern noise removal methods as wavelet or curvlet filtering depends significantly on the numerical combinations and forms of wavelet transform parameters, and the multidimensional extension of such filters is rather non-trivial. These issues are serious obstacle for using of these highly effective filters in the tasks of optoacoustic reconstruction, especially in real laboratorial or medical practice. The objective of our study was to find the optimal filter, convenie

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Zhu, Dan Dan, and Hai Fang Wang. "The Application of Wavelet Denoise in Sampled Grating Comb Filter." Advanced Materials Research 1042 (October 2014): 135–38. http://dx.doi.org/10.4028/www.scientific.net/amr.1042.135.

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This paper based on the theoretical basis of sampled fiber grating, using transfer matrix method to analyze sampled fiber grating. By changing the parameters of sampled fiber grating and simulating its reflective spectrum. Four optical comb filters based on sampled grating were designed. Then the wavelet de-noising principle was introduced. In order to optimize designing of sampling grating comb filter, a filtering analysis and denoising method by using wavelet analysis to denoise was proposed. Finally, the automatic one-dimensional denoising was used with db6 wavelet. It not only denoise refl

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Starosolski, Roman. "Reversible denoising and lifting based color component transformation for lossless image compression." Multimedia Tools and Applications 79, no. 17-18 (2019): 11269–94. http://dx.doi.org/10.1007/s11042-019-08371-w.

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Abstract An undesirable side effect of reversible color space transformation, which consists of lifting steps (LSs), is that while removing correlation it contaminates transformed components with noise from other components. Noise affects particularly adversely the compression ratios of lossless compression algorithms. To remove correlation without increasing noise, a reversible denoising and lifting step (RDLS) was proposed that integrates denoising filters into LS. Applying RDLS to color space transformation results in a new image component transformation that is perfectly reversible despite

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Yang, Yi, and Chengzhen Jia. "Image Denoising and Image Contour Detection." Highlights in Science, Engineering and Technology 131 (March 25, 2025): 14–23. https://doi.org/10.54097/wd660x73.

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With the development of various fields in society, the demand for digital images has increased significantly. These digital images require image processing in many fields before they can be put into use, such as transportation, healthcare and remote sensing. After denoising and contour detection, digital images can play their role. So far, many image denoising and image contour detection methods have been proposed by researchers, each with different characteristics. In the aspect of image denoising, this paper reviews some image-denoising methods and FPGA implementations based on denoising fil

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Zhao, Xue Qing, Xiao Ming Wang, and Qiang Liu. "A High-Performance Filter for Image Denoising Based on Local Features." Applied Mechanics and Materials 182-183 (June 2012): 496–500. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.496.

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A high-performance filter (HPF) is proposed for removing noises in corrupted images, where the local features are adopted to preserve the image local structures. Firstly, the Chebyshev’s theorem and fuzzy mean process are used to adaptively estimate the detection parameters. Secondly, the local statistics theory is used to estimate noisy pixels, which is based on Radon transform. Thirdly, the PSNR is used as the evaluation metric to show the advantage of the HPF, which is compared with latest filters, such as SBF, HPFSM and the classical filter SMF. Extensive experimental results show that the

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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

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Galiano, G., and J. Velasco. "Rearranged nonlocal filters for signal denoising." Mathematics and Computers in Simulation 118 (December 2015): 213–23. http://dx.doi.org/10.1016/j.matcom.2014.11.020.

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Knyazev, Andrew, and Alexander Malyshev. "Accelerated Graph-based Nonlinear Denoising Filters." Procedia Computer Science 80 (2016): 607–16. http://dx.doi.org/10.1016/j.procs.2016.05.348.

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Reddy, Kamireddy Rasool, Madhava Rao Ch, and Nagi Reddy Kalikiri. "Performance Assessment of Edge Preserving Filters." International Journal of Information System Modeling and Design 8, no. 2 (2017): 1–29. http://dx.doi.org/10.4018/ijismd.2017040101.

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Denoising is one of the important aspects in image processing applications. Denoising is the process of eliminating the noise from the noisy image. In most cases, noise accumulates at the edges. So that prevention of noise at edges is one of the most prominent problem. There are numerous edge preserving approaches available to reduce the noise at edges in that Gaussian filter, bilateral filter and non-local means filtering are the popular approaches but in these approaches denoised image suffer from blurring. To overcome these problems, in this article a Gaussian/bilateral filtering (G/BF) wit

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Nex, F., and M. Gerke. "Photogrammetric DSM denoising." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 231–38. http://dx.doi.org/10.5194/isprsarchives-xl-3-231-2014.

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Image matching techniques can nowadays provide very dense point clouds and they are often considered a valid alternative to LiDAR point cloud. However, photogrammetric point clouds are often characterized by a higher level of random noise compared to LiDAR data and by the presence of large outliers. These problems constitute a limitation in the practical use of photogrammetric data for many applications but an effective way to enhance the generated point cloud has still to be found. <br><br> In this paper we concentrate on the restoration of Digital Surface Models (DSM), computed f

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Rubel, Oleksii, Vladimir Lukin, Sergey Abramov, Benoit Vozel, Oleksiy Pogrebnyak, and Karen Egiazarian. "Is Texture Denoising Efficiency Predictable?" International Journal of Pattern Recognition and Artificial Intelligence 32, no. 01 (2017): 1860005. http://dx.doi.org/10.1142/s0218001418600054.

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Images of different origin contain textures, and textural features in such regions are frequently employed in pattern recognition, image classification, information extraction, etc. Noise often present in analyzed images might prevent a proper solution of basic tasks in the aforementioned applications and is worth suppressing. This is not an easy task since even the most advanced denoising methods destroy texture in a more or less degree while removing noise. Thus, it is desirable to predict the filtering behavior before any denoising is applied. This paper studies the efficiency of texture im

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Kaur, Roopdeep, Gour Karmakar, and Muhammad Imran. "Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning." Applied Sciences 13, no. 20 (2023): 11560. http://dx.doi.org/10.3390/app132011560.

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In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for i

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Naimi, Hilal, Amelbahahouda Adamou-Mitiche, and Lahcène Mitiche. "Lifting dual tree complex wavelets transform." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4008. http://dx.doi.org/10.11591/ijece.v11i5.pp4008-4015.

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We describe the lifting dual tree complex wavelet transform (LDTCWT), a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). We describe a way to estimate the accuracy of this approximation and style appropriate filters to attain this. These benefits are often exploited among applications like denois

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Hilal, Naimi, Adamou-Mitiche Amelbahahouda, and Mitiche Lahcène. "Lifting dual tree complex wavelets transform." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4008–15. https://doi.org/10.11591/ijece.v11i5.pp4008-4015.

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We describe the lifting dual tree complex wavelet transform (LDTCWT), a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). We describe a way to estimate the accuracy of this approximation and style appropriate filters to attain this. These benefits are often exploited among applications like denois

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de Oliveira Lyrio, Julio Cesar Soares, Luis Tenorio, and Yaoguo Li. "Efficient automatic denoising of gravity gradiometry data." GEOPHYSICS 69, no. 3 (2004): 772–82. http://dx.doi.org/10.1190/1.1759463.

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Gravity gradiometry data are prized for the high frequency information they provide. However, as any other geophysical data, gravity gradient measurements are contaminated by high‐frequency noise. Separation of the high‐frequency signal from noise is a crucial component of data processing. The separation can be performed in the frequency domain, which usually requires tuning filter parameters at each survey line to obtain optimal results. Because a modern gradiometry survey generates more data than a traditional gravity survey, such time‐consuming manual operations are not very practical. In a

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Ashraf, Raniya, Roz Nisha, Fahad Shamim, and Sarmad Shams. "Cutting through the noise: A Three-Way Comparison of Median, Adaptive Median, and Non-Local Means Filter for MRI Images." Sir Syed University Research Journal of Engineering & Technology 14, no. 1 (2024): 01–06. http://dx.doi.org/10.33317/ssurj.600.

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Medical Imaging is an essential practice in radiology to create high-standard images of the human brain. In medical imaging, denoising techniques are essential during image processing for a meaningful view of the anatomical structure of the images. In order to overcome the denoising issues, various filtering techniques and smoothening algorithms have come forth to get an accurate image for better diagnosis while preserving the original image quality. This work utilizes three computational methods for filtering noise that could distort the factual information in MRI images. The input used as th

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Gautam, Divya, Kavita Khare, and Bhavana P. Shrivastava. "A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising." Applied Sciences 13, no. 12 (2023): 7032. http://dx.doi.org/10.3390/app13127032.

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Medical image denoising is a crucial pre-processing task in the medical field to ensure accurate analysis of anomalies or sicknesses in the human body. Digital filters are popular for reducing undesired noise as they provide reliability, high accuracy, and reduced sensitivity to component tolerances compared to analog filters. However, conventional digital filter design approaches lack efficiency in achieving global optimization robustness. To overcome these incapabilities, this paper adopted bio-inspired optimization algorithms to offer viable digital filter designing tools because of their s

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