• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.184-187
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• 2019

본 논문에서는 영상에서 발생하는 주기적 잡음을 제거하기 위해 다양한 필터들을 이용하여 성능 비교 실험을 수행한다. 영상의 주파수 도메인에서 지역적으로 잡음이 발생하면 영상의 공간 도메인에서 주기적인 잡음이 발생한다. 우선, 영상을 주파수 도메인에서 잡음을 야기시키는 영역을 분석하여 해당 영역에 지역적으로 노치 필터를 적용한다. 이를 통해 영상의 원신호를 유지하면서 영상에서 발생했던 주기적 잡음을 제거함으로써 영상의 화질이 개선됨을 실험을 통해 검증했다. 또한 객관적 지표 비교를 통해 3 가지의 지역적인 노치 필터들의 성능을 비교하고 최적의 필터를 제시한다.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.281-284
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• 2000

이 논문은 웨이브릿 변환 영역의 백색 가우시안 잡음이 부가된 영상에서 최고 대역에서는 Donoho가 제시한 Visushrink 방법으로 잡음을 제거하고 최저대역을 제외한 나머지 대역들은 Monotonic 변환을 이용한 각 대역의 잡음편차를 추정하고 이를 VisuShrink 경계값에 적용하여 Soft-Threshold 기법으로 영상잡음을 제거하는 방법을 제안하였다. 실험 결과 이 논문에서 제시된 혼합방법에 의한 잡음 제거는 Donoho가 제시한 VisuShrink 방법보다 1㏈ 정도의 잡음제거 개선 효과가 있었다.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.175-182
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• 2017

Noises are usually generated by various external causes and low quality devices in image data acquisition and recording as well as by channel interference in image transmission. Since these noise signals result in the loss of information, subsequent image processing is subject to the corruption of the original image. In general, image processing is performed in the mixed noise environment where common types of noise, known to be Gaussian and impulse, are present. This study proposes an iterative weighted mean filter for reducing mixed type of noise. Impulse noise pixels are first turned off in the input image, then $3{\times}3$ sliding window regions are processed by replacing center pixel with the result of weighted mean mask operation. This filtering processes are iterated until all the impulse noise pixels are replaced. Applied to images corrupted by Gaussian noise with ${\sigma}=10$ and different levels of impulse noise, the proposed filtering method improved the PSNR by up to 12.98 dB, 1.97 dB, 1.97 dB respectively, compared to SAWF, AWMF, MMF when impulse noise desities are less than 60%.

• Journal of the Korean Society of Radiology
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• v.8 no.6
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• pp.357-364
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• 2014

The filtered back projection in the image reconstruction algorithms for the clinic computed tomography system has been widely used. Noise of the reconstructed image was examined under the input noise for parallel and fan beam geometries. The reconstruction images of $512{\times}512$ size were carried out under 360 and 720 projection by the Visual C++ for parallel beam and fan beam, respectively, and those agreed with the original Shepp-Logan head phantom very much. Noise was generated because of intrinsic restriction (finite number of projections) for the image reconstruction algorithm, filtered back projection, when no input noise was applied. Because the result noise was rapidly increased under 0.5% input noise ratio, technologies for reducing noise in CT system and image processing is important.

• 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.27 no.1
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• pp.54-61
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• 2023

With the development of IoT technologies and artificial intelligent, diverse digital image equipments are being used in industrial sites. Because image data can be easily damaged by noise while it's obtained with a camera or a sensor and the damaged image has a bad effect on the process of image processing, noise removal is being demanded as preprocessing. In this thesis, for the restoration of image damaged by the noise of random impulse, a switching filter algorithm based on noise estimation was suggested. With the proposed algorithm, noise estimation and error distraction were carried out according to the similarity of the pixel values in the local mask of the image, and a filter was chosen and switched depending on the ratio of noise existing in the local mask. Simulations were conducted to analyze the noise removal performance of the proposed algorithm, and as a result of magnified image and PSNR comparison, it showed superior performance compared to the existing method.

• Journal of IKEEE
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• v.23 no.3
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• pp.910-916
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• 2019

Non-local similarity of natural images is one of highly exploited features in various applications dealing with images. Unique edges, texture, and pattern of the images are frequently repeated over the entire image. Once the similar image blocks are classified into a cluster, representative features of the image blocks can be extracted from the cluster. The bigger the size of the cluster is the better the additive white noise can be separated. Denoising is one of major research topics in the image processing field suppressing the additive noise. In this paper, a denoising algorithm is proposed which first clusters the noisy image blocks based on similarity, extracts the feature of the cluster, and finally recovers the original image. Performance experiments with several images under various noise strengths show that the proposed algorithm recovers the details of the image such as edges, texture, and patterns while outperforming the previous methods in terms of PSNR in removing the additive Gaussian noise.

• KIISE Transactions on Computing Practices
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• v.20 no.11
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• pp.573-578
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• 2014

In this letter, we propose a noise reduction method for use in magnetic resonance images that is based on non-local mean and guided image filters. Our method consists of two phases. In the first phase, the guidance image is obtained from a noisy image by using an adaptive non-local mean filter. The spread of the kernel is adaptively by controlled by implementing the concept of edgeness. In the second phase, the noisy images and the guidance images are provided to the guided image filter as input in order to produce a noise-free image. The improved performance of the proposed method is investigated by conducting experiments on standard datasets that contain magnetic resonance images. The results show that the proposed scheme is superior over the existing approaches.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.112-114
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• 2014

Generally, images are corrupted by the impulse or AWGN and there are cases where both of these noises are added at once. When it comes to eliminating the noises added to the image, the previous median filter is effective in removing the impulse noise and the average filter is effective for removing AWGN. However, when the complex noises are added, it lacks the noise suppression characteristics, thus in this paper, a non-linear filter algorithm for removing the complex noises was proposed. The simulation results shows the proposed algorithm has excellent de-noising capabilities of compare existing methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.325-328
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• 2013

본 논문에서는 ToF(Time-of-Flight) 카메라의 거리 잡음을 제거하는 방법으로 거리와 진폭에 따른 거리 잡음 모델링을 이용한 적응적인 SUSAN(Smallest Univalue Segment Assimilating Nucleus) 필터를 제안한다. ToF 카메라의 거리 잡음 제거를 위해서 기존에 제안된 여러 가지 방법들은 거리 잡음의 특성을 고려하지 않거나 진폭에 따른 거리 잡음의 특성만을 고려하였다. 하지만 실제 ToF 카메라의 거리 영상에 포함되는 거리 잡음은 진폭과 거리에 따라서 변화하기 때문에 거리와 진폭을 모두 고려한 거리 잡음 모델링이 필요하다. 따라서 제안하는 방법은 우선 거리와 진폭의 변화에 따른 ToF 카메라의 거리 잡음 특성을 모델링 한다. 이후 제안하는 방법은 생성된 거리 잡음 모델에 의해 인자가 결정되는 적응적 SUSAN 필터를 이용하여 ToF 카메라의 거리 영상의 잡음을 제거한다. 실험 결과 제안하는 방법은 기존의 ToF 거리 영상의 거리 잡음제거 방법에 비해 보다 효과적으로 거리 영상의 잡음을 제거하면서 디테일을 잘 보존하였다.