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1
Farshi, Taymaz Rahkar. "Image Noise Reduction Method Based on Compatibility with Adjacent Pixels." International Journal of Image and Graphics 17, no. 03 (2017): 1750014. http://dx.doi.org/10.1142/s0219467817500140.
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This paper proposes an efficient noise reduction method for gray and color images that are contaminated by salt-and-pepper noise. In the proposed method, the pixels that are more compatible with adjacent pixels are replaced with target (noisy) pixels. The algorithm is applied on noisy Lena and Mansion images that are contaminated by salt-and-pepper noise with 0.1 and 0.2 noise intensities. Although this method is developed for reducing noise from the images that are contaminated by salt-and-pepper noise, it can also reduce the noise from the images that are contaminated by other types of noises; yet it is more efficient for reducing salt-and-pepper noise. Both numerical and visual comparisons are demonstrated in the experimental simulations. The results show the proposed algorithm can successfully remove impulse noise from images that are contaminated by salt-and-pepper noise.
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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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Azzeh, Jamil, Bilal Zahran, and Ziad Alqadi. "Salt and Pepper Noise: Effects and Removal." JOIV : International Journal on Informatics Visualization 2, no. 4 (2018): 252. http://dx.doi.org/10.30630/joiv.2.4.151.
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Noises degrade image quality which causes information losing and unsatisfying visual effects. Salt and Pepper noise is one of the most popular noises that affect image quality. In RGB color image Salt and pepper noise changes the number of occurrences of colors combination depending on the noise ratio. Many methods have been proposed to eliminate Salt and Pepper noise from color image with minimum loss of information. In this paper we will investigate the effects of adding salt and pepper noise to RGB color image, the experimental noise ratio will be calculated and the color combination with maximum and minimum numbers of occurrence will be calculated and detected in RGB color image. In addition this paper proposed a methodology of salt and pepper noise elimination for color images using median filter providing the reconstruction of an image in order to accept result with minimum loss of information. The proposed methodology is to be implemented, tested and experimental results will be analyzed using the calculated values of RMSE and PSNR.
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Muthmainnah. "Optimized the Performance of Super Resolution Images by Salt and pepper Noise Removal based on a Modified Trimmed Median Filter." Wasit Journal of Computer and Mathematics Science 2, no. 3 (2023): 107–15. http://dx.doi.org/10.31185/wjcms.191.
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Image processing is an interesting area where noisy images can be restored from salt and pepper noise. Various filtering algorithms can be used in the restoration process. Pixels in the original image should not be affected by the restoration process. Despite changes in dimensions and image format, the problem of the existing work persists. A hybrid technique used Ant colony Optimization to remove high-density salt and pepper noise from images. This hybrid technique would remove salt and pepper noise in corrupted images. Ant Colony Optimization (ACO) identifies and selects noisy pixels from corrupted images. It eliminates salt and pepper noise (SP Noise), which causes black and white spots in the original image. All the processes are explained to prove the theory, and the simulation results are presented.
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Abdeljalil Al-Balqa, Dr Jihad N. "An efficient Scheme to Remove Low Density Impulse Noise From A Digital Image." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 12 (2016): 7284–89. http://dx.doi.org/10.24297/ijct.v15i12.4351.
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An improved adaptive noise reduction scheme for images that are highly corrupted by Salt-and-Pepper noise(impulse noise), is proposed in this paper which efficiently removes the salt and pepper noise while preserving the details. The proposed scheme efficiently identifies and reduces salt and pepper noise. The algorithm utilizes an IIR filter with controlled parameters to get better image quality than the existing methods of noise removing. A comparative analysis between the proposed scheme and other techniques of noise reduction or removing is presented in order to show the advantages of the proposed scheme in removing the noisy pixels and producing a better PSNR.
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Konyar, Mehmet Zeki, and Sıtkı Öztürk. "Reed Solomon Coding-Based Medical Image Data Hiding Method against Salt and Pepper Noise." Symmetry 12, no. 6 (2020): 899. http://dx.doi.org/10.3390/sym12060899.
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Medical data hiding is used to hide patient information inside medical images to protect patient privacy. Patient information in the image should be protected when sending medical images to other specialists or hospitals over the communication network. However, the images are exposed to various unwanted disruptive signals in the communication channel. One of these signals is salt and pepper noise. A pixel exposed to salt and pepper noise becomes completely black or completely white. In pixel-based data hiding methods, it is not possible to extract the secret message in the pixel exposed to this kind of noise. While current data hiding methods are good for many disruptive effects, they are weak against salt and pepper noise. For this reason, the proposed study especially focuses on the accurate extraction of patient information in the salt and pepper noisy medical images. This study was proposed for the most accurate extraction of secret message despite salt and pepper noise, by use of a Reed Solomon error control coding-based data hiding method. The most important feature of Reed Solomon codes is that they can correct errors in non-binary (decimal) numbers directly. Therefore, the Reed Solomon coding-based data hiding method that proposed in this study increases the resistance against salt and pepper noise. Experimental studies show that secret data is accurately extracted from stego images with various densities of salt and pepper noise. Stego medical images created by the proposed method have superior quality values compared to similar literature studies. Additionally, compared to similar methods, the secret message is extracted from the noisy stego image with higher accuracy.
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Kwon, Se-Ik, and Nam-Ho Kim. "Salt and Pepper Noise Removal using Histogram." Journal of the Korea Institute of Information and Communication Engineering 20, no. 2 (2016): 394–400. http://dx.doi.org/10.6109/jkiice.2016.20.2.394.
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Yu, Jingning. "Based on Gaussian filter to improve the effect of the images in Gaussian noise and pepper noise." Journal of Physics: Conference Series 2580, no. 1 (2023): 012062. http://dx.doi.org/10.1088/1742-6596/2580/1/012062.
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Abstract Gaussian filter is one of the important research topics. Researchers find that Gaussian filter can suppress noise, but there is still a gap in the completeness of Gaussian filter for denoising. Therefore, the author optimized the Gaussian filter to achieve better filtering effect. By collecting SNR and PSNR data and comparing and analysing different noise types of the data under the same condition, the author explored the denoising effect of Gaussian filter method on Gaussian noise and Salt and pepper noise, and improved the Gaussian filter method. According to the comparison of SNR and PSNR data, the author found that under the default condition, the SNR value of Gaussian noise after optimization is 57.69201, and the SNR value of Salt and pepper noise is 31.5896. The PSNR value of Gaussian noise is 31.5021, and the PSNR value of Salt and pepper noise is 19.7872. The SNR and PSNR values of Gaussian noise are much larger than those of Salt and pepper noise, indicating that the denoising effect of Gaussian filter on Gaussian noise is obviously better than that of Salt and pepper noise under the same condition. The SNR value and PSNR value of Gaussian noise in the two groups of experiments are larger than Salt and pepper noise, which indicates that under the same condition, Gaussian filter is conducive to suppressing Gaussian noise and has a more significant impact on Gaussian noise.
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Nisha, Bernad, and M. Victor Jose. "DTMF: Decision Based Trimmed Multimode Approach Filter for Denoising MRI Images." IT Journal Research and Development 7, no. 2 (2023): 152–72. http://dx.doi.org/10.25299/itjrd.2023.9463.
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The brain MRI image denoising is a challenging and attracting field for young researchers because it enhances the quality of medical images. Salt and pepper noise is the most dangerous noise which reduces the accuracy of brain diagnosis, and it damages the brain medical images severely, that leads to neurologists to fix incorrect treatments or surgery. The pitfalls raised in the existing denoising methods are less Peak signal to noise ratio, high time consumption and incapable for enormous level of noise range. Hence, this research proposes a novel denoising filter which is entitled as ‘Decision based Trimmed Multimode approach oriented Filter (DTMF)’ for salt and pepper noise removal. Herein, the noise removal section is branched into six steps which efficiently reduce noises based on multimode of majority strength. The main concepts used in this research are viz. decision based approach, trimming process, majority of intensity, median, mean, dynamic windows and Square shaped Exemplar Modeled Patch Mechanism (SEMPM). The essential contributions of this approach are i) designing rule set for majority strength structured multimode denoising, ii) computation of majority property oriented parameters like majority-instance, majority strength and majority value, iii) novel SEMPM mechanism to predict noise-free data. The novel SEMPM mechanism grants a solution for the prediction of noise-free pixel in account of the noisy pixels whose surrounding window is completely packed by noisy data. The proposed decision-based approach removes the salt and pepper noise with high peak signal to noise ratio even for huge noise range, with reasonable time consumption.
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Jain, Anshika, and Maya Ingle. "PERFORMANCE ANALYSIS OF NOISE REMOVAL TECHNIQUES FOR FACIAL IMAGES- A COMPARATIVE STUDY." BSSS journal of computer 12, no. 1 (2021): 1–10. http://dx.doi.org/10.51767/jc1201.
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Image de-noising has been a challenging issue in the field of digital image processing. It involves the manipulation of image data to produce a visually high quality image. While maintaining the desired information in the quality of an image, elimination of noise is an essential task. Various domain applications such as medical science, forensic science, text extraction, optical character recognition, face recognition, face detection etc. deal with noise removal techniques. There exist a variety of noises that may corrupt the images in different ways. Here, we explore filtering techniques viz. Mean filter, Median filter and Wiener filter to remove noises existing in facial images. The noises of our interest are namely; Gaussian noise, Salt & Pepper noise, Poisson noise and Speckle noise in our study. Further, we perform a comparative study based on the parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index Method (SSIM). For this research work, MATLAB R2013a on Labeled faces in Wild (lfw) database containing 120 facial images is used. Based upon the aforementioned parameters, we have attempted to analyze the performance of noise removal techniques with different types of noises. It has been observed that MSE, PSNR and SSIM for Mean filter are 44.19 with Poisson noise, 35.88 with Poisson noise and 0.197 with Gaussian noise respectively whereas for that of Median filter, these are 44.12 with Poisson noise, 46.56 with Salt & Pepper noise and 0.132 with Gaussian noise respectively. Wiener filter when contaminated with Poisson, Salt & Pepper and Gaussian noise, these parametric values are 44.52, 44.33 and 0.245 respectively. Based on these observations, we claim that the Median filtering technique works the best when contaminated with Poisson noise while the error strategy is dominant. On the other hand, Median filter also works the best with Salt & Pepper noise when Peak Signal to Noise Ratio is important. It is interesting to note that Median filter performs effectively with Gaussian noise using SSIM.
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Sudha, T., and P. Nagendra Kumar. "Performance Analysis of Median Filter With Respect to Different Padding Methods in the Context of Removing Salt and Pepper Noise." Asian Journal of Computer Science and Technology 8, no. 3 (2019): 6–9. http://dx.doi.org/10.51983/ajcst-2019.8.3.2732.
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Image Processing is one of the major areas of research. Images are often corrupted with different types of noise such as Gaussian noise, Poisson noise, Salt and Pepper noise, Speckle noise etc.The present work analyses the performance of the median filter with respect to different padding methods in the context of removing salt and pepper noise.Peak Signal-to-Noise ratio and Mean Squared Error have been considered as parameters for performance evaluation. The results obtained show thatthe Peak Signal-to-Noise Ratio and Mean Squared Error obtained between the original image and the filtered image obtained by applying median filter with symmetric padding method on the image corrupted with salt and pepper noise is same as the Peak Signal-to-Noise Ratio and Mean Squared Error obtained between the original image and the filtered image obtained by applying median filter with replicate padding method on the image corrupted with salt and pepper noise respectively.
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Chen, Fengyu, Minghui Huang, Zhuxi Ma, Yibo Li, and Qianbin Huang. "An Iterative Weighted-Mean Filter for Removal of High-Density Salt-and-Pepper Noise." Symmetry 12, no. 12 (2020): 1990. http://dx.doi.org/10.3390/sym12121990.
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Salt-and-pepper noise, which is often introduced by sharp and sudden disturbances in the image signal, greatly reduces the quality of images. Great progress has been made for the salt-and-pepper noise removal; however, the problem of image blur and distortion still exists, and the efficiency of denoising requires improvement. This paper proposes an iterative weighted-mean filter (IWMF) algorithm in detecting and removing high-density salt-and-pepper noise. Three steps are required to implement this algorithm: First, the noise value and distribution characteristics were used to identify the noise pixels, effectively improving the accuracy of noise detection. Second, a weighted-mean filter was applied to the noisy pixels. We adopted an un-fixed shape symmetrical window with better detail preservation ability. Third, this method was performed iteratively, avoiding the streak effect and artifacts in high noise density. The experimental results showed that IWMF outperformed other state-of-the-art filters at various noise densities, both in subjective visualization and objective digital measures. The extremely fast execution speed of this method is quite suitable for real-time processing.
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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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Wang, Xiong Liang, and Chun Ling Wang. "Salt-and-Pepper Noise Removal Based on Image Patch Reordering." Applied Mechanics and Materials 701-702 (December 2014): 352–56. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.352.
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A new method based on image patch reordering for removing salt-and-pepper noise from corrupted images is presented. Firstly, the problem of salt-and-pepper noise removal can be turned into the problem of image in-painting. Then, we can use the image patch reordering method to recover the missing pixels and fulfill the salt-and-pepper noise removal. Experimental results demonstrate that the proposed method obtain much better performance in terms of both qualitative and quantitative assessment. Especially, the proposed method provides the improvement in the performance of noise suppression and detail preservation.
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Kumar, Nalin, and M. Nachamai. "Noise Removal and Filtering Techniques used in Medical Images." Oriental journal of computer science and technology 10, no. 1 (2017): 103–13. http://dx.doi.org/10.13005/ojcst/10.01.14.
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Noise removal techniques have become an essential practice in medical imaging application for the study of anatomical structure and image processing of MRI medical images. To report these issues many de-noising algorithm has been developed like Weiner filter, Gaussian filter, median filter etc. In this research work is done with only three of the above filters which are already mentioned were successfully used in medical imaging. The most commonly affected noises in medical MRI image are Salt and Pepper, Speckle, Gaussian and Poisson noise. The medical images taken for comparison include MRI images, in gray scale and RGB. The performances of these algorithms are examined for various noise types which are salt-and-pepper, Poisson, speckle, blurred and Gaussian Noise. The evaluation of these algorithms is done by the measures of the image file size, histogram and clarity scale of the images. The median filter performs better for removing salt-and-pepper noise and Poisson Noise for images in gray scale, and Weiner filter performs better for removing Speckle and Gaussian Noise and Gaussian filter for the Blurred Noise as suggested in the experimental results.
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Song, Yin Mao, and Xiao Juan Li. "Salt and Pepper Noise Removal Based on GA-BP Algorithm." Advanced Materials Research 301-303 (July 2011): 1243–48. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.1243.
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Noise detection-based median filters have been widely adopted to reduce salt and pepper noise in images. However, since noise pixel is not detected accurately, it is likely to blur the fringe of image under the high noise density. In this paper, we propose an algorithm of salt and pepper noise filter which is based on GA-BP algorithm noise detector to remove the salt and pepper noise in images. The algorithm firstly detect the location of noise pixels by using optimized GA-BP network,then,it introduce edge-preserving function and PRP algorithm to solve the objective function of extreme value further to realize the image denoising. Compared with the traditional algorithms, experimental results show that the proposed algorithm has an evident improvement, and have good characters of generalization, robust and self-adaptive.
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Ye, Xiao Ling, Yan Yan Dou, and Bo Liu. "Adaptive Switching Median Filter Based on GA-BP Neural Network." Advanced Materials Research 753-755 (August 2013): 2980–84. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2980.
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For the poor performance of conventional filtering algorithms in removing salt and pepper noise from digital images under high noise density,an adaptive switching median filter algorithm based on BP neural network optimized by genetic algorithm (GA) is proposed to detect and remove salt and pepper noise from images. Firstly,the initial weights and thresholds of BP neural network are optimized by genetic algorithm.Then image pixels are devided into either signal or noise points by the trained network automatically. The detected noise points will be removed by adaptive switching median filter algorithm,but nothing to do with the signal points. Experiment results show that the proposed algorithm significantly outperforms the others and efficiently removes salt and pepper noise from digital images without distorting image details and textures .
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Chung, Young-Su, and Nam-Ho Kim. "High Density Salt and Pepper Noise Removal using Clustering." Journal of the Korea Institute of Information and Communication Engineering 27, no. 5 (2023): 635–42. http://dx.doi.org/10.6109/jkiice.2023.27.5.635.
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Zaini, Hatim, and Ziad Alqadi. "Improving Average and Median Filters." International Journal of Computer Science and Mobile Computing 12, no. 2 (2023): 1–13. http://dx.doi.org/10.47760/ijcsmc.2023.v12i02.001.
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Average and median filters are the most popular filters used to reduce the negative effects of salt and pepper noise affected gray and color images, these filters work good when the noise affected the image has a small noise ratio. Average and median filters has some disadvantages, which are negatively affect the quality of the denoised image, they treat all the pixels regardless the pixel is a clean pixel or a noisy pixel. In this paper research an improvements will be added to the two filters making them capable to handle salt and pepper noise with high noise ratios. The improvements will be based on using a special mask and special logical index matrices to detect and process only noisy pixels. The improved modified filters will be implemented using noisy images with various noise ratios, the obtained quality factor of the modified filters will be compared with the results of using average and median filters to prove the enhancements provided by the proposed filters.
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Dong, Jing, Zhi Chai, and Ke Wen Xia. "A Combination Approach to Noise Reduction in Digital Image Processing." Applied Mechanics and Materials 278-280 (January 2013): 1359–65. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1359.
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In order to reduce Gaussian and Salt & Pepper noises, a combination approach to noise reduction is presented by combining the median filter with the mean filter. The detail simulations show that the mode which the median filtering first and then the mean filtering is superior to that of the simply single filtering, or the mean filtering first and then the median filtering when the image obviously contain the Salt & Pepper noise. On the other hand, it is not necessarily the optimal scheme to use the mode which the mean filtering first and then the median filtering when the digital image obviously contains the Gaussian noise.
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Abu Zalata, Mohamad K., Hussein N. Hatamleh, and Ziad A. Alqadi. "Detailed Study of Low Density Salt and Pepper Noise Removal from Digital Color Images." International Journal of Computer Science and Mobile Computing 11, no. 2 (2022): 56–67. http://dx.doi.org/10.47760/ijcsmc.2022.v11i02.007.
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Color digital images are greatly affected by salt and pepper noise, which negatively affects the clarity of the image and its characteristics. In this paper research we will investigate various filter used to deal with salt and pepper noise, a comparison will be done between these filters according to the calculated values of MSE and PSNR. An improvement procedure will be added to each filter to show how this improvement will enhance the values of MSE and PSNR and thus enhance the quality of the denoised image. Various noise densities will be experimented and it will be shown how median filter can be highly recommended to reduce the negative effects of salt and pepper noise.
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Halder, Amiya, Sayan Halder, Samrat Chakraborty, and Apurba Sarkar. "A Statistical Salt-and-Pepper Noise Removal Algorithm." International Journal of Image and Graphics 19, no. 01 (2019): 1950006. http://dx.doi.org/10.1142/s0219467819500062.
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This paper proposes a novel approach to remove salt-and-pepper noise from a given noisy image. The proposed algorithm is based on statistical quantities such as mean and standard deviation. It determines the intensity to be placed on the impulse point by calculating the eligibility of the nearby points in a very simple way. This method works iteratively and removes all the impulse points restoring the edges and minute details. The proposed algorithm is very efficient and gives better results than various existing algorithms. The performance of the proposed method are compared with other existing methods with images of noise density as high as 99% and is found to perform better.
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Irviantina, Syanti, and Irpan Pardosi. "Salt and Pepper Noise Removal dengan Spatial Median Filter dan Adaptive Noise Reduction." Jurnal SIFO Mikroskil 17, no. 2 (2016): 127–36. http://dx.doi.org/10.55601/jsm.v17i2.348.
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Noise (gangguan) pada citra tidak hanya terjadi karena ketidaksempurnaan dalam proses pengambilan gambar ataupun pada saat proses transmisi. Melainkan juga dikarenakan kotoran-kotoran yang terjadi pada sebuah citra. Ada beberapa noise yang dapat melekat pada sebuah citra dan salah satunya adalah Salt and Pepper noise yang berupa titik-titik hitam atau??? putih yang tersebar pada sebuah citra. Banyak metode yang dapat digunakan untuk mereduksi atau mengurangi noise yang terdapat pada citra, dan dalam jurnal ini metode yang dapat digunakan dalam mengurangi salt and pepper noise??? ini adalah metode spatial median filter dan adaptive noise reduction. Spatial Median Filter adalah sebuah noise removal filter yang baru dan merupakan sebuah algoritma smoothing yang teratur dengan tujuan untuk menghilangkan noise pada pada sebuah citra. Sementara itu, adaptive noise reduction fokus pada proses pendeteksian salt and pepper noise secara efektif dan mengembalikan citra digital secara efisien. Aplikasi yang dihasilkan dapat digunakan untuk melakukan perbaikan kualitas gambar dengan citra berupa grayscale ataupun citra berwarna (RGB). Selain itu, juga disediakan sebuah metric pengukuran untuk menguji apakah citra dari hasil reduksi noise lebih baik daripada citra original. Dalam pengujian ini juga dapat diketahui bahwa citra hasil reduksi noise dengan menggunakan Spatial Median Filter memiliki kualitas yang lebih bagus dalam mereduksi noise dibandingkan dengan metode Adaptive Noise Reduction jika dilihat dari perbandingan hasil nilai MSE dan PSNR citra asli dengan citra hasil reduksi. Juga dapat dilihat bahwa semakin besar noise yang dimiliki oleh sebuah citra, maka nilai PNSR yang didapatkan akan semakin kecil. ???
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Sathuluri, Ramarao, and Srinivas L. "An Advanced New Adaptive Alpha Trimmed Median Filter for Removel of Salt and Pepper Noises for Recovering the Image Protecting Edge Data." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 733–43. https://doi.org/10.31142/ijtsrd13026.
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Here in this paper we discuss about an advanced new adaptive alpha trimmed median filter for removel of salt and pepper noises for recovering the image protecting edge data. In olden days we used anlage technology to capture the images which manes photo reals or negatives to capture the images, even though they have high resolution when compared when compared to the today digital images, we have to compromise the parameters like lighting, and some other noises. When compared to the disadvantages of the analogue technology digital technology is preferred. In the digital technology we can edit or modify or even we can compress the images without losing visual effects. When coming to the digital technology there are some complications like noise, size issue, etc. While considering the noise there are different types of noises like Salt and Pepper noise, Gaussian noise, etc. Salt and Pepper noise is the frequently observed noise, in which some the pixel values are disturbed and changes into pure white or pure black and while considering the colour images it changes maximum or minimum of corresponding colour. This creates the disturbances in the images in different levels. Higher the damaged pixels higher the noise appears. In our proposed system we are using a new filter called Alpha trimmed median filter. The pixels which effected by noise have the value 0 or 255 which are black and white respectively. In olden methods they mostly used the median filter to rectify or recover the noise images, even though they give the resultant output in rectifications it also results in the loss of edge data. In our proposed system we are changing the value of only corrupted pixel values which results in recovering the image and protecting the edge data. Sathuluri Ramarao | L. Srinivas "An Advanced New Adaptive Alpha-Trimmed Median Filter for Removel of Salt and Pepper Noises for Recovering the Image Protecting Edge Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd13026.pdf
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Kwon, Se-Ik, and Nam-Ho Kim. "Salt and Pepper Noise Removal using Cubic Spline Interpolation." Journal of the Korea Institute of Information and Communication Engineering 20, no. 10 (2016): 1955–60. http://dx.doi.org/10.6109/jkiice.2016.20.10.1955.
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Jung, Gyeong-Seog, and Nam-Ho Kim. "RANSAC Switching Filter for Salt and Pepper Noise Removal." Journal of the Korea Institute of Information and Communication Engineering 28, no. 2 (2024): 146–53. http://dx.doi.org/10.6109/jkiice.2024.28.2.146.
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Mesuga, Reymond, Cloyd Raymond Pernes, Luther Villacruz, and Mark Anthony Burgonio. "INVESTIGATION ON THE EFFECTS OF RADIOGRAPHIC IMAGE QUALITY ATTRIBUTES ON THE PERFORMANCE OF CONVOLUTIONAL NEURAL NETWORKS (CNNS) IN DETECTING COVID-19." PUP Journal of Science and Technology 14, no. 1 (2024): 15–35. https://doi.org/10.70922/zt7bfe34.
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Radiographic image quality is one of the factors that impacts professionals’ decisions when diagnosing lung diseases using X-ray images. Hence, poor radiographic image quality could result in a misleading diagnosis affecting the person being investigated. This is true in human vision, as well as the computer vision. This study investigated the effects of different radiographic image quality attributes (i.e., contrast, Gaussian blur, Gaussian noise, and salt-and-pepper noise) on the performance of various Convolutional Neural Networks (CNNs) models. We use COVID-19 x-ray data as an initiative to the pandemic, apply different radiographic image quality attributes, and test the performance of CNN models in the effects of the attributes in the classification task. The results showed the following: (i) increasing levels of experimented noises (i.e., Gaussian and salt-and-pepper noise) rapidly decreases the performance of the models with no sign of resiliency; (ii) decreasing contrast appears to be beneficial at some particular level (e.g., contrast factor = 3); and (iii) increasing Gaussian blur decreases the performance of models but less rapidly than that of noises. As a conclusion, increasing noise like Gaussian and salt-and-pepper noise can be considered as a hindrance to the performance of CNNs while decreasing contrast and increasing Gaussian blur seemed to be beneficial especially if applied for data augmentation or enhancement techniques as the performance of the CNNs were observed to be more resilient against these two attributes than that of noises.
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Zaini, Hatim, and Ziad Alqadi. "High Salt and Pepper Noise Ratio Reduction." International Journal of Computer Science and Mobile Computing 10, no. 9 (2021): 88–97. http://dx.doi.org/10.47760/ijcsmc.2021.v10i09.009.
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Colored digital images are affected by salt and pepper noise, affecting their clarity, contents and properties. The negative effect on the image increases with the increase in the noise level. Filters based on average and median filters are not able to remove SAPN with high noise ratios, and accordingly, blurred images are obtained that cannot be dealt with in various image processing operations. In this paper research a modification will be add to median and average filters making them capable of reducing the noise even it has a high noise ratio, the modified average and median filters will be implanted and some comparisons with other popular filters will be made to show the enhancement of the modified filters.
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Ali, Suhad A., C. Elaf A. Abbood, and Shaymaa Abdu LKadhm. "Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (2016): 2219. http://dx.doi.org/10.11591/ijece.v6i5.11641.
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<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>
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Ali, Suhad A., C. Elaf A. Abbood, and Shaymaa Abdu LKadhm. "Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (2016): 2219. http://dx.doi.org/10.11591/ijece.v6i5.pp2219-2224.
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<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>
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Liu, Hongqing, Liming Hou, Zhen Luo, Yi Zhou, Xiaorong Jing, and Trieu-Kien Truong. "Image Recovery with Data Missing in the Presence of Salt-and-Pepper Noise." Applied Sciences 9, no. 7 (2019): 1426. http://dx.doi.org/10.3390/app9071426.
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In this paper, an image recovery problem under the case of salt-and-pepper noise and data missing that degrade image quality is addressed if they are not effectively handled, where the salt-and-pepper noise as the impulsive noise is remodeled as a sparse signal due to its impulsiveness and the data missing pattern, denoted by a sparse vector, contains only zeros and ones to formulate the data missing. In particular, the salt-and-pepper noise and data missing are reformatted by their sparsity, respectively. The wavelet and framelet domains are explored to sparsely represent the image in order to accurately reconstruct the clean image. From the reformulations conducted and to recover the image, under one optimization framework, a joint estimation is developed to perform the image recovery, the salt-and-pepper noise suppression, and the missing patter estimation. To solve the optimization problem, two efficient solvers are developed to obtain the joint estimation solution, and they are based on the alternating direction method of multipliers (ADMM) and accelerated proximal gradient (APG). Finally, numerical studies verify that the joint estimation algorithm outperforms the state-of-the-art approaches in terms of both objective and subjective evaluation standards.
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Sahni, Supreet. "Removal of Image Blurring and Salt Pepper Noise Using Variation Models." International Journal of Scientific Engineering and Research 5, no. 10 (2017): 29–33. https://doi.org/10.70729/ijser171865.
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N. Hussin, Kholood, Ali K. Nahar, and Hussain Kareem Khleaf. "A hybrid bat-genetic algorithm for improving the visual quality of medical images." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 220. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp220-226.
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Efficient repression of noise in a medical image is a very significant issue. This paper proposed a method to denoise medical images by the use of a hybrid adaptive algorithm based on the bat algorithm (BA) and genetic algorithm (GA). Medical images can be often affected by different kinds of noise that decrease the precision of any automatic system for analysis. Therefore, the noise reduction methods are always utilized for increasing the Peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) of images to optimize the originality. Gaussian noise and salt and pepper noise corrupted the used medical data, separately. The noise level to medical images was added noise variance from 0.1 to 0.5 to compare the performance of the de-noising techniques. In the analytical study, we apply different kinds of noise like Gaussian noise and salt-and-pepper noise to medical images for making these images noisy. The hybrid BA-GA model was applied on medical noisy images to eliminate noise and the performances have been determined by the statistical analyses such as PSNR, values are gotten 63.04 dB and 59.75 dB for CT and MRI images.
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Hussin, Kholood N., Ali K. Naha, and Kareem Hussain. "A hybrid bat-genetic algorithm for improving the visual quality of medical images." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 220–26. https://doi.org/10.11591/ijeecs.v28.i1.pp220-226.
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Efficient repression of noise in a medical image is a very significant issue. This paper proposed a method to denoise medical images by the use of a hybrid adaptive algorithm based on the bat algorithm (BA) and genetic algorithm (GA). Medical images can be often affected by different kinds of noise that decrease the precision of any automatic system for analysis. Therefore, the noise reduction methods are always utilized for increasing the Peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) of images to optimize the originality. Gaussian noise and salt and pepper noise corrupted the used medical data, separately. The noise level to medical images was added noise variance from 0.1 to 0.5 to compare the performance of the de-noising techniques. In the analytical study, we apply different kinds of noise like Gaussian noise and salt-and-pepper noise to medical images for making these images noisy. The hybrid BA-GA model was applied on medical noisy images to eliminate noise and the performances have been determined by the statistical analyses such as PSNR, values are gotten 63.04 dB and 59.75 dB for CT and MRI images.
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Vairaprakash, S., Jayakumar Karuppaiah, B. Buvaneswari, and Abdullah Alghamdi. "Removal of Salt and Pepper Noise Using Hybrid Adaptive Switching Median Filter with Ant Colony Optimization Technique in Nano Electronic Applications." Journal of Nanoelectronics and Optoelectronics 18, no. 1 (2023): 85–94. http://dx.doi.org/10.1166/jno.2023.3365.
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Background: Restoration of noisy images from the salt and pepper noise is an interesting area in the field of image processing. The restoration process can be done using various filtering algorithms. The restoration process should not affect the pixels of the original image. The problem of the existing work persists as the increase in the error rate while the dimensions as well as the image format changes. The proposed work consists of Hybrid Adaptive Switching median filtering (HASMF). The hybrid technique corrupted images’ high-density salt and pepper noise removal using Ant colony Optimization technique. This hybrid technique would remove the high-density salt and pepper noise from the corrupted images. The noisy pixel value from the corrupted images is identified and selected using the Ant Colony Optimization technique (ACO). The identified corrupted value can be replaced using the Adaptive Switching Median Filter. The switching process is carried out using the pixel by pixel with the normalized median values. The noisy pixels are identified and selected using Ant colony Optimization. The optimized values are subjected to the filtering process. The proposed method decreases the salt and pepper noise within the original image. The hybrid design approach was used in the proposed study, which used 45 nm technology combined with a Verilog-A model-based circuit that was implemented using Spintronic. It was discovered that the suggested changed task had less latency, used less space, and dissipated less power than the original. Furthermore, it was discovered that designed memory arrays were both energy and space-efficient. It does not affect the normal pixels within the original image. The comparison process has been made with the various existing algorithms such as Median Filter (MF), Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF). The proposed method has overcome the various performance metrics such as Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index (SSIM). The results obtained have shown the significant results in terms of object measures as well as visual perception of the denoised image.
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Singh, Abhishek, and Anil Kumar. "Introduction of Local Spatial Constraints and Local Similarity Estimation in Possibilistic c-Means Algorithm for Remotely Sensed Imagery." Journal of Modeling and Optimization 11, no. 1 (2019): 51–56. http://dx.doi.org/10.32732/jmo.2019.11.1.51.
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This paper presents a unique Possibilistic c-Means with constraints (PCM-S) with Adaptive Possibilistic Local Information c-Means (ADPLICM) in a supervised way by incorporating local information through local spatial constraints and local similarity measures in Possibilistic c-Means Algorithm. PCM-S with ADPLICM overcome the limitations of the known Possibilistic c-Means (PCM) and Possibilistic c-Means with constraints (PCM-S) algorithms. The major contribution of proposed algorithm to ensure the noise resistance in the presence of random salt & pepper noise. The effectiveness of proposed algorithm has been analysed on random “salt and pepper” noise added on original dataset and Root Mean Square Error (RMSE) has been calculated between original dataset and noisy dataset. It has been observed that PCM-S with ADPLICM is effective in minimizing noise during supervised classification by introducing local convolution.
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C, Shankara, and A. Hariprasad S. "Noise Removal Techniques for Lung Cancer CT Images." Indian Journal of Science and Technology 15, no. 32 (2022): 1577–86. https://doi.org/10.17485/IJST/v15i32.798.
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Abstract <strong>Objectives:</strong> To analyze various filtering methods to eliminate noises present in the lung CT images and to enhance the image, which help in further evaluation of CT images for accurate lung cancer detection. To compare the proposed method with existing filtering techniques and to find the best filtering technique. <strong>Methods:</strong> For input lung CT images noises like salt along with pepper noise and speckle noise are added. For noisy images different filtering methods like Median filter, Wiener filter, Gaussian filter and Guided filter are applied. The performances of different filters are computed in terms of metrics for evaluation like PSNR, SSIM, MSE, and SNR. Based on the performance metrics the best filter is selected to remove noise in the lung CT images. <strong>Findings:</strong> The results of the experiment shows that the median filter is more efficient in comparison to other filtering methods in eliminating noises that exist in lung CT images by owning fewer mean square error (MSE) value of 4.065604, a high SNR value of 36.5931, a high SSIM value of 0.983545, and high PSNR value of 42.0395. <strong>Novelty:</strong> Different filtering methods are analyzed for different noise densities from 5% to 50% and chosen best filter by considering different evaluation metrics. The proposed method is compared with existing filtering techniques. The method can be used for elimination of noise in the other imaging modalities. <strong>Keywords:</strong> Filtering; Median filter; Wiener filter; Gaussian filter; Salt and Pepper noise; Speckle noise
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Lingamaiah Kurva, Naga, and S. Varadarajan. "Dual tree complex wavelet transform based image denoising for Kalpana satellite images." International Journal of Engineering & Technology 7, no. 3.29 (2018): 269. http://dx.doi.org/10.14419/ijet.v7i3.29.18810.
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This paper presents a new algorithm to reduce the noise from Kalpana Satellite Images using Dual Tree Complex Wavelet Transform technique. Satellite Images are not simple photographs; they are pictorial representation of measured data. Interpretation of noisy raw data leads to wrong estimation of geophysical parameters such as precipitation, cloud information etc., hence there is a need to improve the raw data by reducing the noise for better analysis. The satellite images are normally affected by various noises. This paper mainly concentrates on reducing the Gaussian noise, Poisson noise and Salt & Pepper noise. Finally the performance of the DTCWT wavelet measures in terms of Peak Signal to Noise Ratio and Structural Similarity Index for both noisy & denoised Kalpana images.
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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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Hou, Ming Liang, Yu Ran Liu, and Qi Wang. "An Image Information Extraction Algorithm for Salt and Pepper Noise on Fractional Differentials." Advanced Materials Research 179-180 (January 2011): 1011–15. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.1011.
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An image information extraction algorithm on fractional differentials is put forward in this paper that is based on the characteristics of fractional differential in signal processing. This paper has extracted the information of salt and pepper noise images with various coefficients, and analyzed and compared it with the information extraction results of classic integer-order operators as Prewitt, Roberts and Sobel. Experiments have shown that not only the high-frequency marginal information can be extracted by extracting information with fractional differentials, just as it is extracted with integer-order operators, but the texture information can also be extracted from the smooth region. Besides, this algorithm is featured with great noise immunity against salt and pepper noises.
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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.
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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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Zayed, M. Ramadan. "Optimum Image Filters for Various Types of Noise." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 5 (2018): 2458–64. https://doi.org/10.12928/TELKOMNIKA.v16i5.10508.
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In this paper, the quality performance of several filters in restoration of images corrupted with various types of noise has been examined extensively. In particular, Wiener filter, Gaussian filter, median filter and averaging (mean) filter have been used to reduce Gaussian noise, speckle noise, salt and pepper noise and Poisson noise. Many images have been tested, two of which are shown in this paper. Several percentages of noise corrupting the images have been examined in the simulations. The size of the sliding window is the same in the four filters used, namely 5x5 for all the indicated noise percentages. For image quality measurement, two performance measuring indices are used: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The simulation results show that the performance of some specific filters in reducing some types of noise are much better than others. It has been illustrated that median filter is more appropriate for eliminating salt and pepper noise. Averaging filter still works well for such type of noise, but of less performance quality than the median filter. Gaussian and Wiener filters outperform other filters in restoring mages corrupted with Poisson and speckle noise.
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Kinge, Sanjaykumar, B. Sheela Rani, and Mukul Sutaone. "Restored texture segmentation using Markov random fields." Mathematical Biosciences and Engineering 20, no. 6 (2023): 10063–89. http://dx.doi.org/10.3934/mbe.2023442.
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<abstract> <p>Texture segmentation plays a crucial role in the domain of image analysis and its recognition. Noise is inextricably linked to images, just like it is with every signal received by sensing, which has an impact on how well the segmentation process performs in general. Recent literature reveals that the research community has started recognizing the domain of noisy texture segmentation for its work towards solutions for the automated quality inspection of objects, decision support for biomedical images, facial expressions identification, retrieving image data from a huge dataset and many others. Motivated by the latest work on noisy textures, during our work being presented here, Brodatz and Prague texture images are contaminated with Gaussian and salt-n-pepper noise. A three-phase approach is developed for the segmentation of textures contaminated by noise. In the first phase, these contaminated images are restored using techniques with excellent performance as per the recent literature. In the remaining two phases, segmentation of the restored textures is carried out by a novel technique developed using Markov Random Fields (MRF) and objective customization of the Median Filter based on segmentation performance metrics. When the proposed approach is evaluated on Brodatz textures, an improvement of up to 16% segmentation accuracy for salt-n-pepper noise with 70% noise density and 15.1% accuracy for Gaussian noise (with a variance of 50) has been made in comparison with the benchmark approaches. On Prague textures, accuracy is improved by 4.08% for Gaussian noise (with variance 10) and by 2.47% for salt-n-pepper noise with 20% noise density. The approach in the present study can be applied to a diversified class of image analysis applications spanning a wide spectrum such as satellite images, medical images, industrial inspection, geo-informatics, etc.</p> </abstract>
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Zhao, Feng, Rui Chuan Ma, and Jia Qing Ma. "An Algorithm for Salt and Pepper Noise Removal Based on Information Entropy." Applied Mechanics and Materials 220-223 (November 2012): 2273–79. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2273.
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By using information entropy to estimate the distribution uniformity of the pixels with a same gray level, an accurate salt and pepper noise detection method is presented based on the statistical property of salt and pepper noise. And then, a new modified mean filter is designed, which sets up noise-centre filtering windows, Moreover, the weighted means are calculated by merely using the non-noise points in each filtering window. The presented filter can efficiently preserve the details of images, avoid the affection of noise points on the restore points, and reduce the dimness of the noise points. Experimental results show that this algorithm has the better performance on noise detection, noise filtering, and the protection of detail.
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Zhang, Houwang, Yuan Zhu, and Hanying Zheng. "NAMF: A Nonlocal Adaptive Mean Filter for Removal of Salt-and-Pepper Noise." Mathematical Problems in Engineering 2021 (March 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/4127679.
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In this paper, a novel algorithm called a Nonlocal Adaptive Mean Filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented. We employ an efficient window detector with adaptive size to detect the noise. The noisy pixel is then replaced by the combination of its neighboring pixels, and finally, a SAP noise based nonlocal mean filter is used to reconstruct the intensity values of noisy pixels. Extensive experimental results demonstrate that NAMF can obtain better performance in terms of quality for restoring images at all levels of SAP noise.
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Jia, Fang, De Cheng Xu, and Xin Fu. "An Improved Hybrid Median-Mean Filter Algorithm." Applied Mechanics and Materials 701-702 (December 2014): 288–92. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.288.
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In the process of imaging, digitalization and transmission, images are generally contaminated by Gaussian noise and salt & pepper noise, which cannot be eliminated completely at the same time only by Mean filter or Median filter. Aiming at solving this problem, an improved hybrid median-mean filter algorithm based on the Improved Median Filtering (IMF) algorithm is proposed in this paper. The experimental results show that the new algorithm shows better performance than either Median filtering algorithm or Mean filtering algorithm, which can not only get rid of Gaussian noise and salt & pepper noise simultaneously, but also minimize the contradictions between noise erasing and image details protecting effectively.
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He, Chun, Ke Guo, and Huayue Chen. "An Improved Image Filtering Algorithm for Mixed Noise." Applied Sciences 11, no. 21 (2021): 10358. http://dx.doi.org/10.3390/app112110358.
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In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as Gaussian noise, salt and pepper noise, multiplicative noise, and so on. For mixed noise removal, such as salt and pepper noise + Gaussian noise, although some methods are currently available, the denoising effect is not ideal, and there are still many places worthy of improvement and promotion. To solve this problem, this paper proposes a filtering algorithm for mixed noise with salt and pepper + Gaussian noise that combines an improved median filtering algorithm, an improved wavelet threshold denoising algorithm and an improved Non-local Means (NLM) algorithm. The algorithm makes full use of the advantages of the median filter in removing salt and pepper noise and demonstrates the good performance of the wavelet threshold denoising algorithm and NLM algorithm in filtering Gaussian noise. At first, we made improvements to the three algorithms individually, and then combined them according to a certain process to obtain a new method for removing mixed noise. Specifically, we adjusted the size of window of the median filtering algorithm and improved the method of detecting noise points. We improved the threshold function of the wavelet threshold algorithm, analyzed its relevant mathematical characteristics, and finally gave an adaptive threshold. For the NLM algorithm, we improved its Euclidean distance function and the corresponding distance weight function. In order to test the denoising effect of this method, salt and pepper + Gaussian noise with different noise levels were added to the test images, and several state-of-the-art denoising algorithms were selected to compare with our algorithm, including K-Singular Value Decomposition (KSVD), Non-locally Centralized Sparse Representation (NCSR), Structured Overcomplete Sparsifying Transform Model with Block Cosparsity (OCTOBOS), Trilateral Weighted Sparse Coding (TWSC), Block Matching and 3D Filtering (BM3D), and Weighted Nuclear Norm Minimization (WNNM). Experimental results show that our proposed algorithm is about 2–7 dB higher than the above algorithms in Peak Signal-Noise Ratio (PSNR), and also has better performance in Root Mean Square Error (RMSE), Structural Similarity (SSIM), and Feature Similarity (FSIM). In general, our algorithm has better denoising performance, better restoration of image details and edge information, and stronger robustness than the above-mentioned algorithms.
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Al Faruq, Umar, and Homa P. Harahap. "Analisa Kinerja Algoritma Detektor Sudut pada Citra Noise Komparasi Operator (Moravec, Susan, Haris, FAST, Eigen dan Forstner)." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 15, no. 1 (2018): 126–39. http://dx.doi.org/10.33751/komputasi.v15i1.1268.
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Algoritma detektor medeskripsikan komparasi kinerja dari masing-masing algoritma pendeteksi sudut pada citra noise. Citra yang digunakan sebagai masukan adalah citra dengan format grayscale (citra abu-abu) dan diberikan beberapa jenis noise. Algoritma yang dibandingkan adalah algoritma Moravec, Susan, Harris, FAST, Eigen dan Forstner. Jenis noise yang akan di gunakan adalah gaussian, poisson, salt &#38; pepper dan speckle. Hasil pengujian didapatkan sebagai berikut; detektor Moravec mengahasilkan 1.000 titik sudut pada citra noise (gaussian, poisson, salt and pepper, speckle), rata-rata waktu proses pendeteksian sebesar 2,19 detik. Detektor Susan menghasilkan 100 titik sudut pada citra noise (gaussian, poisson, salt and pepper dan speckle) dengan rata-rata waktu proses pendeteksian sebesar 23,99 detik. Hasil pengujian akurasi setiap detektor sudut pada citra yang memilki noise menyatakan bahwa; seluruh detektor sudut tidak mampu menemukan titik-titik sudut dengan tepat, seluruh detektor sudut tidak akurat dalam menunjukkan lokasi titik sudut, hanya detektor Moravec dan Susan yang stabil terhadap perulangan, seluruh detektor sudut tidak stabil atau sangant sensitif terhadap semua tipe noise. Hasil pengujian dalam penelitian ini memperlihatkan bahwa seluruh detektor sudut sangat sensitif terhadap noise, dengan pengertian lain bahwa tingkat akurasi hasil pendeteksian setiap detektor sudut akan sangat dipengaruhi oleh noise dan tipe noise.Kata Kunci: Detektor Sudut, Titik Sudut, Grayscale, Noise, Moravec, Susan, Harris, FAST, Eigen dan Forstner
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Limbong, Hanspran, Lailan Sofinah Harahap, and Rafli Arya Gading. "Comparison of Median Filter and Gaussian Filter Performance in Removing Salt and Pepper Noise." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 3 (2025): 1849–54. https://doi.org/10.59934/jaiea.v4i3.1033.
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Image processing plays a critical role in various applications, from medical diagnostics to surveillance systems. However, one of the major challenges in digital image processing is the presence of noise, particularly salt and pepper noise, which significantly degrades image quality. This study aims to compare the effectiveness of two popular filtering techniques—Median Filter and Gaussian Filter—in removing salt and pepper noise from digital images. The evaluation is conducted both quantitatively, using Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) metrics, and qualitatively, through visual analysis. The experimental results show that the Median Filter consistently outperforms the Gaussian Filter in terms of noise reduction performance. Median filtering yields higher PSNR and lower MSE values across various levels of noise intensity (5%, 10%, and 15%). Moreover, the visual assessment indicates that Median Filter preserves image edges and fine details more effectively, whereas Gaussian Filter tends to introduce blurring artifacts due to its smoothing nature. These findings suggest that for impulsive noise such as salt and pepper, Median Filter is a more appropriate and robust method, offering better restoration quality without compromising important image features.
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Puji Sri Alhirani, Lailan Sofinah Harahap, and Rani Chantika. "Analisis Perbandingan Filter Median dan Gaussian dalam Mengurangi Noise pada Citra Digital." Jurnal Ilmiah Teknik Informatika dan Komunikasi 5, no. 2 (2025): 495–505. https://doi.org/10.55606/juitik.v5i2.1148.
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. Digital image processing is one of the important aspects in the world of technology, especially in improving image quality from noise interference. This study aims to analyze and compare the performance of the Median Filter and Gaussian Filter in reducing salt & pepper noise in digital images. The research process was carried out using the Python programming language and the OpenCV and NumPy libraries. The initial image was randomly noised, then processed using both types of filters. The results obtained were evaluated visually and quantitatively using the PSNR (Peak Signal-to-Noise Ratio) and MSE (Mean Squared Error) metrics. Based on the experimental results, the Median Filter was able to produce cleaner images and maintain image details compared to the Gaussian Filter. These results indicate that the Median Filter has advantages in handling salt & pepper noise. This study is expected to be a reference in selecting the right filtering method to improve the quality of digital images.