• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3166-3179
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• 2013

A pre-processing system for adaptive noise removal is proposed based on the principle of identifying and filtering object regions and background regions. Human perception of images depends on bright, well-focused object regions; these regions can be treated with the best filters, while simpler filters can be applied to other regions to reduce overall computational complexity. In the proposed method, bright region segmentation is performed, followed by segmentation of object and background regions. Noise in dark, background, and object regions is then removed by the median, fast bilateral, and bilateral filters, respectively. Simulations show that the proposed algorithm is much faster than and performs nearly as well as the bilateral filter (which is considered a powerful noise removal algorithm); it reduces computation time by 19.4 % while reducing PSNR by only 1.57 % relative to bilateral filtering. Thus, the proposed algorithm remarkably reduces computation while maintaining accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.79-86
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• 2002

Nowadays there is a trend to apply the diffusion equation to image Processing. The anisotropic diffusion equation is highly favoured as a noise removal algorithm because it can remove noise while enhancing edges. However if the two dimensional anisotropic diffusion equation is applied to the noise removal of video sequences, flickering artifact due to the luminance difference between frames and ghost artifact due to the interfiltering between frames occur. In this paper the two dimensional anisotropic diffusion equation is extended to the sequence axis. The Proposed three dimensional anisotropic diffusion equation removes noise more efficiently than the two dimensional equation, and furthermore removes the flickering and ghost artifact as well.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.397-400
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• 2016

In digital images, the addition due to noise occurs in the process of obtaining, saving, and transmitting. For examples of noise, there are salt and pepper noise, Gaussian noise, and composition noise where various noises are mixed. Existing filters have insufficient noise removal characteristics because it uses single filters in composite noise environment. Therefore the study suggested a switching filter that processes with special weighted value and median filter according to local mask salt and pepper noise density when central pixel is damaged by salt and pepper noise, and processes by applying weighted values differently according to standard deviation of local mask when damaged by Gaussian noise.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.169-173
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• 2018

Iris diagnosis based on the color and texture information is one of a novel approach which can represent the current state of a certain organ inside body or the health condition of a person. In analysis of the iris images, there are critical image artifacts which can prevent of use interpretation of the iris textures on images. Here, we developed the iris diagnosis system based on a hand-held typed imaging probe which consists of a single camera sensor module with 8M pixels, two pairs of 400~700 nm LED, and a guide beam. Two original images with different light noise pattern were successively acquired in turns, and the light noise-free image was finally reconstructed and demonstrated by the proposed artifact removal approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.394-400
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• 2019

Noise removal is one of the required step in processing digital video and there are many researches to develop algorithm that fits with its purpose and environment. However, present impulse noise removal methods are lacking in its function in terms of removing noise in edge and high frequency factors. Therefore, this research has Extended range of masks depending on density to determine noise so that high frequency factors can be preserved. The range of resolution is set based on median and standard deviation of inside resolution after removing impulse noise. afterwards, those resolution within the range are calculated by adding weight to have the final output value. The suggested algorithm has an enhanced function in removing noise in various areas with many edge and high frequency factors than present methods and their functions are compared through simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.989-995
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• 2022

Currently, the demand for image processing is increasing due to the development of IT technology, and active research is being conducted. Since image data generates image noise due to various external causes, and thus degrades the performance of the image, noise removal is essential. Salt and Pepper noise is a representative image noise, and various studies are being conducted to remove it. Existing algorithms include A-TMF, AFMF, LIWF, but these have the disadvantage that their performance is somewhat insufficient. Therefore, in this paper, we propose an algorithm that performs filtering using linear interpolation with effective pixels existing around the central pixel only in case of noise after performing noise judgment in order to efficiently remove salt and pepper noise. In order to judge the performance of the proposed algorithm, it was compared using the processed image of the previously studied algorithm and PSNR.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.441-443
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• 2022

Currently, image processing is being used in various fields such as image analysis, image recognition, and factory automation according to the development of IT technology. Salt and pepper noise is generated due to various external factors in the process of acquiring or transmitting an image, which deteriorates the image quality. Therefore, noise removal to improve image quality is essential. Various methods have been proposed to remove salt and pepper noise, and representative examples include AF, MF, and A-TMF. However, the conventional filter has insufficient noise removal performance in a high-density noise environment. Therefore, in this paper, we propose an algorithm for estimating and processing the original pixel by using the modified distance weight filter only in the case of noise, and replacing the original pixel in case of non-noise after performing noise judgment. To evaluate the performance of the proposed algorithm, we compare and analyze it with existing algorithms using PSNR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3873-3887
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• 2018

In this paper, we propose a novel method to recover images corrupted by impulse noise. The proposed method uses two stages: noise detection and filtering. In the first stage, we use pixel values, rank-ordered logarithmic difference values, and median values to train a neural-network-based impulse noise detector. After training, we apply the network to detect noisy pixels in images. In the next stage, we use group-based weighted couple sparse representation to filter the noisy pixels. During this second stage, conventional methods generally use only clean pixels to recover corrupted pixels, which can yield unsuccessful dictionary learning if the noise density is high and the number of useful clean pixels is inadequate. Therefore, we use reconstructed pixels to balance the deficiency. Experimental results show that the proposed noise detector has better performance than the conventional noise detectors. Also, with the information of noisy pixel location, the proposed impulse-noise removal method performs better than the conventional methods, through the recovered images resulting in better quality.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.167-170
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• 2005

Recently, filtering methods for attenuating noise while preserving image details are in progress actively. And SM(standard median) filter showed a great performance for noise removal in impulse noise environment but, it caused edge cancellation error. So, variable methods that modified SM(standard median) filter have been proposed, and CWM(center weighted median) filter is representative. Also, there are several methods to improve the efficiency based on min/max operation in term of preserving detail and filtering speed. In this paper, we managed a pixel corrupted by impulsive noise using min/max value of the surrounding band enclosing a pixel, and compared the efficiency with exiting methods in the simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1725-1731
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• 2013

Recently, as industrial society is developed as high digital information age, image processing is used in a variety of areas. However, image deterioration is still occurred by several noises in the data processing, and different kinds of researches are in progress for noise removal. Therefore, this paper limited the modified median filter by using standard deviation to removes impulse noise. The proposed algorithms divided into noise detection and noise removal, the noise free pixel is maintained, the noise pixel filtered. And it was compared with the existing methods for its objective judgment, using PSNR(peak signal to noise ratio) as standard criteria.