• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1215-1220
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• 2016

As society has developed rapidly toward a highly advanced digital information age, a multimedia communication service for acquisition, transmission and storage of image data as well as voice has being commercialized. However, image data is always corrupted by various noises during image processing, so researches for removing noises have been continued until now. 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 linear interpolation, histogram weighted values and median filter 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. In addition, the algorithm was compared with the conventional methods for objectively and used the PSNR(peak signal to noise ratio) as the basis of the determination.

• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.878-888
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• 2016

This paper proposes an efficient method to remove the boundary artifacts of rendered views caused by damaged depth maps in the 3D video system. First, characteristics of boundary artifacts with the compression noise in depth maps are carefully studied. Then, the artifacts suppression method is proposed by the iterative projection onto convex sets (POCS) algorithm with setting the convex set in pixel and frequency domain. The proposed method is applied to both texture and depth maps separately during view rendering. The simulation results show the boundary artifacts are greatly reduced with improving the quality of synthesized views.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.7
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• pp.1696-1705
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• 1996

The theory of stack filtering, which is a generalization of median filtering, is used to the detection of intensity edges in noisey images. The proposed approach, called the Difference of Estimates(DoE) approach, is a new formulation of a morphological scheme which has been very sensitive to impulse noise. In this approach, stack filters are applied to a noisy image to obtain local estimates of the dilated and eroded versions of the noise-free image. Thresholding the difference between these two estimates yields the binary edge map. We find that this approach yields results comparable to those obtained with the Canny operator for images with additive Gaussian noise, burt works much better when the noise is impulsive.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.11-19
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• 2023

In this paper, it is confirmed that the metrics used to evaluate image quality can be applied to low-light images. Due to the nature of low-illumination images, factors related to light create various noise patterns, and the smaller the amount of light, the more severe the noise. Therefore, in situations where it is difficult to obtain a clean image without noise, the quality of a low-illuminance image from which noise has been removed is often judged by the human eye. In this paper, noise in low-illuminance images for which ground truth cannot be obtained is removed using Noise2Noise, and spatial resolution and radial resolution are evaluated using ISO 12233 charts and colorchecker as metrics such as MTF and SNR. It can be shown that the quality of the low-illuminance image, which has been evaluated mainly for qualitative evaluation, can also be evaluated quantitatively.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.43-48
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• 2011

Among various methods proposed earlier, fuzzy image filtering is usually one of the favored techniques because it has less blurring effect and the decrease of noise removal rate after filtering. However, fuzzy filtering is ineffective on color images since it is firstly developed with gray scale. Thus, in this paper, we propose a fuzzy filtering algorithm for color images. First, we divide RGB color information from image into three channels of R, G, and B and judge the possibility of each pixel with mask by fuzzy logic independently. The output pixel value might be the average or median according to the degree of noise. Our experiment successfully verifies the effectiveness of new algorithm in color image.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.123-126
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• 1999

일반적으로 영상신호는 푸리에 분석법에 기초를 두고 있는 DST를 이용하여 압축된다. 그러나 DCT는 블록변환의 범주에 속하기 때문에 영상신호를 높은 압축률로 압축하거나 압축한 영상신호를 잡음이 존재하는 통신채널을 통하여 전송하면 복원한 영상신호의 품질을 떨어뜨리는 블록현상이 수신측에서 나타난다. 본 논문에서는 웨이블릿을 이용하여 잡음에 강한 특성을 가지는 영상신호의 압축방법을 연구하였다. 서로 다른 서브밴드에 속하는 웨이블릿 계수들은 직교 웨이블릿에 의하여 생성되었지만, 변환 방향과 원천이 같은 계수들의 경우 서로 종속적인 관계를 가지므로 이러한 특징을 이용하여 영상신호를 압축하였다. 웨이블릿을 이용하여 압축된 영상신호는 최근 고속의 데이터 전송을 가능하게 하여서 많은 고속 통신 서비스의 기초 기술로 각광을 받고 있는 OFDM 시스템을 이용하여 전송하였다.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.981-984
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• 2001

본 논문은 이동물체 영역을 신뢰성 있게 분리하는데 기초가 되는 seed를 정확하게 선정하고, 선정된 seed를 중심으로 영역을 확장함으로써 이동물체 영역을 분리하기 위한 방법을 제안한다. 고정된 카메라로부터 입력되는 연속된 영상열로부터 초기의 이동물체가 존재하지 않는 영상을 참고영상으로 하여 입력영상과의 차영상을 구하고 차영상의 히스토그램에서 배경잡음 모델링을 통해 배경잡음을 제거한다. 그리고 배경잡음이 제거된 차영상에서 Local Maxima 들을 이용해 후보 seed를 선정한 후, 이드의 특징값들을 분석하여 이동물체의 seed와 배경의 seed 를 결정하고 이 두 개의 seed를 기반으로 watershed 알고리즘을 적용하여 영역을 확장함으로써 이동물체 영역을 추출한다. 제안된 방법을 실제 상황에서 얻은 다양한 영상열에 적용한 결과, 기존의 영역분리 알고리즘보다 주위 잡음의 영향을 적게 받으며 효과적으로 이동물체를 분리할 수 있음을 확인할 수 있었다.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.725-737
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• 2021

Although the performance of cameras is gradually improving now, there are noise in the acquired digital images from the camera, which acts as an obstacle to obtaining high-resolution images. Traditionally, a filtering method has been used for denoising, and a convolutional neural network (CNN), one of the deep learning techniques, has been showing better performance than traditional methods in the field of image denoising, but the details in images could be lost during the learning process. In this paper, we present a CNN for image denoising, which improves image details by learning the details of the image based on wavelet transform. The proposed network uses two subnetworks for detail enhancement and noise extraction. The experiment was conducted through Gaussian noise and real-world noise, we confirmed that our proposed method was able to solve the detail loss problem more effectively than conventional algorithms, and we verified that both objective quality evaluation and subjective quality comparison showed excellent results.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.233-245
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• 2008

In this work, a new adaptive weighted mean filter is proposed for preserving image details while effectively suppressing impulse noise. The proposed filter is based on a noise pixel detection-estimation strategy. All the pixels are first detected using an impulse noise detector. Then the detected noise pixels are replaced with the output of the weighted mean filter over adaptive working window according to the rate of corrupted neighborhood pixels, while noise-free pixels are left unaltered. We compare the proposed filter to other existing filters in the qualitative measure and quantitative measures such as PSNR and MAE as well as computation time to verify the capability of the proposed filter. Extensive simulations show that the proposed filter performs better than other filters in impulse noise suppression and detail preservation without increasing of running time.

• 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.