• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.120-125
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• 2009

For effective noise removal prior to video processing, noise power or noise variance of an input video sequence needs to be found exactly, but it is actually a very difficult process. This paper presents an accurate noise variance estimation algorithm based on motion compensation between two adjacent noisy pictures. Firstly, motion estimation is performed for each block in a picture, and the residue variance of the best motion-compensated block is calculated. Then, a noise variance estimate of the picture is obtained by adaptively averaging and properly scaling the variances close to the best variance. The simulation results show that the proposed noise estimation algorithm is very accurate and stable irrespective of noise level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.8
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• pp.2001-2007
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• 2014

Image signal related technology has been developing via various display equipment development and popularization of contents. However, errors occur in these image contents due to addition of excess noise from several cause during the process of general image signal data processing, transmission and storage. In terms of noise added to the image content, there are various types in accordance with cause of occurrence and form, and it is typically impulse noise, gaussian noise and complex noise which is composed of two types of overlapping noise. In this paper, complex algorithm is suggested in order to lessen the effect of mixed noise added to the image content by putting it through noise judgement process and categorizing each into impulse and gaussian noise and processing them separately. And in order to demonstrate the superiority of the suggested algorithm, PSIN(peak signal to noise ratio) was used as the standard of judgement.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.270-277
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• 2010

The BAMS filter is a kind of wavelet shrinkage filter based on the Bayes estimators with no simulation, therefore it can be used for a real time filter. The denoising efficiency of BAMS filter is seriously affected by the estimated noise variance in each wavelet band. To remove noise in signals in existing BAMS filter, the noise variance is estimated by using the quartile of the finest level of details in the wavelet decomposition, and with this variance, the noise of the level is removed. In this paper, to remove the image noise includingodified quartile of the level of detail is proposed. And by these techniques, the image noises of mid and high frequency bands are removed, and the results showed that the increased PSNR of ab the midband noise, the noise variance estimation method using the monotonic transform and the mout 2[dB] and the effectiveness in denosing of low noise deviation images.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.526-532
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• 2022

With the development of IoT technology, image processing is being utilized in various fields such as image analysis, image recognition, medical industry, and factory automation. Noise is generated in image data from causes such as defect in transmission line. Image noise must be removed because it damages the performance of the image processing application program. Salt and Pepper noise is a representative type of image noise, and various studies have been conducted to remove Salt and Pepper noise. Widely known methods include A-TMF, AFMF, and SDWF. However, as the noise density increases, the performance deteriorates. Thus, this paper proposes an algorithm that performs filtering using a fuzzy logic weight mask only in case of noise after noise determination. In order to prove the noise removal performance of the proposed algorithm, an experiment was performed on images with 10% to 90% noise added and the PSNR was compared.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.455-462
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• 2015

Image media is used for the internet, computers and digital cameras as part of the core services of multimedia. Digital images can be easily acquired and processed, due to the development of digital home appliances and personal computers' application software. However, image degradation occurs by various external causes in the acquisition, processing and transmitting process of digital images, and its main cause is known to be noise. Therefore, this study proposed and conducted the simulation of image restoration filter algorithm that processes impulse noise and Gaussian noise by applying Lagrange interpolation and spatial weighted method according to distance, respectively. The proposed algorithm improved 8.77[dB], 8.83[dB] and 10.02[dB], respectively, compared to existing A-TMF, AWMF and MMF, as a result of processing by applying the damaged Girl images to impulse noise(P=60%) and Gaussian noise(${\sigma}=10$).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.230-236
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• 2015

In these digital times, the image signal processing is being used in various areas like vehicle recognition, security, and robotics. Generally, the image deterioration occurs by salt & pepper noise in the procedures of image transmission, storage, and processing. Methods to remove this noise are SMF, CWMF, and SWMF and these methods have few unsatisfactory noise reduction characteristics in salt & pepper noise environment. Therefore, in order to mitigate salt & pepper noise which is added in the image, this study suggested an algorithm which subdivides the masks in the image into four areas and processes using non-noise pixel numbers in each area. Additionally, in order to prove the excellence of the proposed algorithm, relevant performances were compared with existing methods using PSNR.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.869-878
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• 2009

Image restoration techniques such as noise reduction and contrast enhancement have been researched for enhancing a contaminated image by the noise. An image degraded by additive random noise can be enhanced by noise reduction. Sigma filtering is one of the most widely used method to reduce the noise. In this paper, we propose a new sigma filter algorithm based on noise variance estimation which effectively enhances the degraded image by noise. Specifically, the Bartlett test is used to measure the degree of noise with respect to the degree of image feature. Simulation results are also given to show the performance of the proposed algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.598-604
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• 2012

Image signal is corrupted by various noises in image processing, many studies are being accomplished to restore those images. In this paper, we proposed a cascade filter algorithm for removing random valued impulse noise. The algorithm consists two steps that noise detection and noise elimination. Variance of filtering mask and center pixel variance are calculated for noise detection, and the noise pixel is replaced by estimated value which first apply switching self adaptive weighted median filter and finally processed by modified weight filter. Considering the proposed algorithm only remove noise and preserve the uncorrupted information that the algorithm can not only remove noise well but also preserve edge.

• 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.