• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.1-8
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• 2005

There are many image segmentation methods having bon published as the results of research so far, however these are for the noise images which can process an image under the general white noise environments. Therefore, these methods has the disadvantages because it is difficult to extract only the accurate parameters, which can distinguish between image and impulsive noise, from image with impulsive noises. So it has a problem about the potential decreasing of the performance according to the impulsive noise for all applications using the present quadtree segmentation. In this paper, we propose new quadtree segmentation using quartile deviation which can extract effectively the image information parameters from a noise image. Therefore our method can apply for various image processing fields because it has a advantage to distinguish an image information from noise image. As the result of simulation, we confirm that the proposed quadtree segmentation is more efficient than the present method when tested on impulsive noise image.

• Journal of the Korean Society for Railway
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• v.13 no.3
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• pp.283-289
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• 2010

Recent advance of digital camera results in that image signal processing techniques are widely adopted to railroad security management. However, due to the nature of railroad management many images are acquired in low light level environment such as night scenes. The lack of light causes lots of noise in the image, which degrades image quality and causes errors in the next processes. 3D noise reducing techniques produce better results by using consecutive sequence of images. On the other hand, they cause degradation such as motion blur if there are motions in the sequence. In this paper, we use an adaptive weight filter to estimate more accurate motions and use the result of the adaptive filter to 3D result to improve objective and subjective mage quality.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.321-324
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• 2011

본 논문에서는 contourlet 변환을 이용하여 잡음을 제거하는 방법을 제안한다. 영상 센서의 발전으로 이미지의 해상도가 좋아지는 반면 잡음에 민감해진다. 그러므로 이를 전처리 단계에서 처리해주는 것이 필요하다. 잡음은 주로 자연 영상의 윤곽선에서 민감하게 반응하기 때문에 고주파대의 잡음을 최대한 정확하게 제거하는 과정이 중요하다. Contourlet 변환은 기존의 wavelet 변환의 다중 스케일과 더불어 다양한 방향 필터뱅크를 이용하여 방향 성분에 대하여 풍부한 정보를 얻을 수 있는 변환이다. 영상의 화이트 가우시안 잡음을 제거하기 위해 contourlet 변환 영역에서의 계수를 이변수 가우스 확률 모델로 설정하고 Bayes 추정법을 사용한다. Bayes 추정법에 필요한 파라미터들은 근사적으로 추정한다. 제안한 방식을 통하여 잡음이 제거된 영상에 추가적으로 Wiener filter와 cycle-spinning을 적용하여 더 높은 PSNR (peak signal-to-noise ratio)값을 얻을 수 있다. 모의실험을 통해 제안한 방식의 PSNR 값과 결과영상으로 성능이 우수함을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.20-21
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• 2018

행렬의 rank 최소화 기법은 영상 잡음 제거, 행렬 완성(completion), low rank 행렬 복원 등 다양한 영상처리 분야에서 효과적으로 이용되어 왔다. 특히 nuclear norm 을 이용한 low rank 최소화 기법은 convex optimization 을 통하여 대상 행렬의 특이값(singular value)을 thresholding 함으로써 간단하게 low rank 행렬을 얻을 수 있다. 하지만, nuclear norm 을 이용한 low rank 최소화 방법은 행렬의 rank 값을 정확하게 근사하지 못하기 때문에 잡음 제거가 효과적으로 이루어지지 못한다. 본 논문에서는 영상의 잡음을 제거 하기 위해 다중 잡음 제거 영상을 이용하여 유사도가 높은 유사 패치 행렬을 구성하고, 유사 패치 행렬의 rank 를 non-convex function 을 이용하여 최소화시키는 방법을 통해 잡음을 제거하는 방법을 제안한다.

• Journal of Korea Multimedia Society
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• v.5 no.2
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• pp.147-157
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• 2002

Noise in image sequences is visually offensive and may mask important image detail. In addition to degradation of visual quality, the noise pattern increases the entropy of the image, and thus hinders effective compression. This paper proposes a spatial and a temporal joint filters to reduce the noise by jointly connecting two adaptive noise reducers with different characteristics, and we also propose an IIR-type 3D noise reduction litter scheme connecting the spatial and the temporal joint filters. The proposed 3D IIR filter not only strongly removes noise in uniform image regions while preserving edges and details but also effectively suppresses temporal flicker caused by noise. Experimental results show that the proposed scheme improves subjective quality as well as objective quality as compared with the various noise filtering techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.727-734
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• 2014

Digital processing technologies are being studied in various areas of image compression, recognition and recovery. However, image deterioration still occurs due to the noises in the process of image acquisition, storage and transmission. Generally in the typical noises which are included in the images, there are Gaussian noise and the mixed noise where the Gaussian noise and impulse noise are overlapped and in order to remove these noises, various researches are being executed. In order to preserve the edge and effectively remove mixed noises, image recovery filter algorithm was suggested in this study which sets and processes the adaptive weight using the median values and average values after noise judgment. Additionally, existing methods were compared through simulations and PSNR(peak signal to noise ratio) was used as a judgment standard.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.813-821
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• 2020

Recently, research using unstructured data such as images and videos has been actively conducted in various fields. Edge detection is one of the most useful image enhancement techniques to improve the quality of the image process. However, it is very difficult to perform edge detection in noise images because the edges and noise having high frequency components. This paper uses a linear model and standard deviation as an effective edge detection method for noise images. The edge is detected by the difference between the standard deviation of the pixels included in the pixel block and the standard deviation of the residual obtained by fitting the linear model. The results of edge detection are compared with the results of the Sobel edge detector. In the original image, the Sobel edge detection result and the proposed edge detection result are similar. Proposed method was confirmed that the edge with reduced noise was detected in the various levels of noise images.

• 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 Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1431-1436
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• 2020

In modern society, various video devices such as CCTV and black boxes are used for convenience. However, noise is frequently generated in the process of transmitting and receiving video images and video signals photographed at night. If such noise is not eliminated, the problem that the image is difficult to identify is generated. Accordingly, noise elimination of images in the image information is an indispensable step. Salt and Pepper noises are typical impulse noises among image noises. Previous research has been carried out as a method for eliminating noise, and CWMF, MMF and A-TMF are typical methods. In common, such a filter exhibits excellent performance in a low-density noise area, but a disadvantage is that noise elimination performance in a high-density noise area is somewhat insufficient. Accordingly, the proposed algorithm uses the inflection point of the histogram graph to separate areas and remove singular points, and proposes a weighting filter utilizing histogram distribution. PSNR was used for objective judgment.

• 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.