• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.429-431
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• 2019

Noise in image processing has a direct effect on the quality of the image, and adversely affects the processing of the system including algorithms such as image segmentation, edge detection, and image recognition. Therefore, noise reduction plays an important role in the preprocessing process. In this paper, we propose an efficient algorithm to remove noise in high density of Salt and Pepper noise. The proposed algorithm removes noise by gradually expanding the filtering mask according to the density of the noise, and shows excellent noise cancellation performance even in a high density region. In order to evaluate the performance of the proposed algorithm, we compared and analyzed the existing method and the proposed algorithm through simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.8
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• pp.1973-1980
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• 2014

Edge detection for such as image, lane and object recognition is important image processing method. And some traditional method for this, there are Sobel, Prewitt, Roberts, Laplacian, LoG(Laplacian of Gaussian) and so on. Characteristics of these methods are insufficient in the salt & pepper noise added image. In order to improve such a problem of conventional methods, in this paper, we proposed an algorithm applying the weighted mask for detecting an edge by setting the local mask centered on the adjacent of the central pixel if central pixel of the mask is non-noise, it is intactly set by element of estimated mask, after calculating estimated mask if it is noise.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.2
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• pp.57-68
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• 2023

Because salt and pepper noise is a type of impulse, even a small amount of noise could cause a large image degradation. In this paper, we proposed a salt-and-pepper noise removal method using the convolutional neural network. It consists of four phases. In the first step, the proposed method reconstructs noisy image using a traditional salt-and-pepper noise reduction method, and in the second step, the result image of previous step is filtered with Gaussian low pass filter. After that, we reconstruct the filtered image using convolution neural network. In the last step, the pixels with salt-and-pepper noise are replaced with the result of previous phase. Simulation results show that the proposed method yields not only objective image qualities(PSNR, SSIM) but also subjective image qualities for all SAP noise ratios.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.65-71
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• 2017

Digital images and videos are subject to various types of noise during storage and transmission. Among these noises, Salt & Pepper noise degrades the compression efficiency of the original data and causing deterioration of performance in edge detection or segmentation used in an image processing method. In order to mitigate this noise, there are many filters such as Median Filter, Weighted Median Filter, Center Weighted Median Filter, Switching Weighted Median Filter and Adaptive Median Filter. However these methods are inferior in performance at high noise density. In this paper we propose a new type of filter for noise mitigation in wireless communication environment where Salt & Pepper noise occurs. The proposed filter detects the location of the damaged pixel by Salt & Pepper noise detection and mitigates the noise by using adjacent pixel values which are not damaged in a certain area. Among the proposed filters, the performance of the filter using the $3{\times}3$ error mask is compared with that of the conventional methods and it is confirmed that when density of noise in the image is 95%, their performances are improved as 13.24 dB compared to MF and 13.09 dB compared to AMF.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.647-653
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• 2016

Recently, the data image processing has been applied to a variety of fields including broadcasting, communication, computer graphics, medicine, and so on. Generally, the image data may develop the noise during their transmission. Therefore, the studies have been actively conducted to remove the noise on the image. There are diverse types of noise on the image including salt and pepper noise, AWGN, and mixed noise. Hence, the filter algorithm for the image recovery was proposed that salt and pepper noise was processed by histogram and spatial weighted values after defining the noise to lessen the impact of mixed noise added in the image, and AWGN was processed by the pixel information of local mask establishing the weighted values in this study. Regarding the processed results by applying Lena images which were corrupted by salt and pepper noise(P=50%) and AWGN(${\sigma}=10$), suggested algorithm showed the improvement by 7.06[dB], 10.90[dB], 5.97[dB] respectively compared with the existing CWMF, A-TMF, AWMF.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1255-1261
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• 2020

Currently, the spread of mobile devices is gradually increasing. Accordingly, various techniques using images or photos are actively being researched. However, image data generates noise for complex reasons, and the accuracy of image processing increases according to the performance of removing noise. Therefore, noise reduction is one of the essential steps. Salt and pepper noise is a typical impulse noise in the image, and various studies are being conducted to remove the noise. However, existing algorithms have poor noise rejection performance in high frequency areas, and average filters have blurring. Therefore, in this paper, we propose an algorithm that effectively removes salt and pepper noise in the high frequency region as well as the low frequency region using entropy. For objective and accurate judgment of proposed algorithms, MSE and PSNR were used to compare and analyze existing algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.113-120
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• 2020

With the recent development of communication equipment, the demand for communication equipment is gradually increasing. Accordingly, various signal processing has been studied. In the case of an image, noise removal is an indispensable step because noise propagation problems may occur if noise is not removed in the pre-processing process. Salt and Pepper noise is a typical impulse noise with two extremes. Various studies have been conducted to remove such noise, and there are CWMF, MF and MMF. However, the existing methods are somewhat insufficient in the high-density noise region. Therefore, in this study, we have proposed an algorithm that filters the size of the mask according to the number of noises inside the 7×7 mask and filters it with a modified switching filter using the histogram distribution of the image. In the case of the proposed algorithm, noise can be effectively removed in a high-density noise region. For objective judgment, PSNR was used to compare and analyze with existing algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.787-789
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• 2014

Image processing is presently used in various areas such as smart phone, smart TV, and portable PC. Likewise, edge detection plays an important role in most of the applications. As such, studies for the detection of edge are continually underway. Roberts, Laplacian and LoG(lapacian of Gaussian) are the representative edge detection methods, but these methods do not offer optimal edge detection characteristic in case of the image that is damaged by Salt & Pepper noises. As such, this study presented algorithm with superior edge detection characteristic by utilizing the elements of local mask in Salt & Pepper noise environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.474-480
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• 2020

With the advent of the Fourth Industrial Revolution, many new technologies are being utilized. In particular, video signals are used in various fields. However, when transmitting and receiving video signals, salt and pepper noise and additive white Gaussian noise (AWGN) occur for multiple reasons. Failure to remove such noise when performing image processing can cause problems. Generally, filters such as CWMF, MF, and AMF remove noise. However, these filters perform somewhat poorly in the high-density noise domain and cause smoothing, resulting in slightly lower retention of the edge components. In this paper, we propose an algorithm by effectively eliminating salt and pepper noise using a modified weight filter using standard deviation. In order to prove the noise reduction performance of the proposed algorithm, we compared it with the existing algorithm using PSNR and magnified images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.115-117
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• 2021

Currently, as IoT technology develops, monitoring systems are being used in various fields, and image processing is being used in various forms. Image data causes noise due to various causes during the transmission and reception process, and if it is not removed, loss of image information or error propagation occurs. Therefore, denoising images is essential. Typical methods of eliminating Salt and Pepper noise in images include AF, MF, and A-TMF. However, existing methods have the disadvantage of being somewhat inadequate in high-density noise. Therefore, in this paper, we propose an algorithm for determining noise for Salt and Pepper denoising and replacing the central pixel with an original pixel if it is non-noise, and processing the filtering mask by segmenting and averaging it in eight directions. We evaluate the performance by comparing and analyzing the proposed algorithms with existing methods.