• 디지털산업정보학회논문지
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• 제8권3호
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• pp.41-48
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• 2012

In this paper, we propose a method of edge detection for noisy image. The proposed method uses a progressive filter for noise reduction and a Sobel operator for edge detection. The progressive filter combines a median filter and a modified rational filter. The proposed method for noise reduction adjusts rational filter direction according to an edge in the image which is obtained by median filtering. Our method effectively attenuates the noise while preserving the image details. Edge detection is performed by a Sobel operator. This operator can be implemented by integer operation and is therefore relatively fast. Our proposed method not only preserves edge, but also reduces noise in uniform region. Thus, edge detection is well performed. Our proposed method could improve results using further developed Sobel operator. Experimental results show that our proposed method has better edge detection with correct positions than those by existing median and rational filtering methods for noisy image.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.434-436
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• 1992

In this paper, we propose a new multilevel nonlinear filter for simultaneous edge detection and noise suppression, which we call a order statistic-median hybrid(OMH) filler. The median-related filters cause an edge shift in the presence of an impulse near the edge. The proposed filter reduces such edge shifting while suppressing impulsive as well as nonimpulsive noise. We show that at the noisy edge point the OMH filter is substantially superior to the median filter, the $\alpha$-TM filter and the STM filter[I] in two respects: (a) the output bias error and (b) the output mean square error. Test results confirm that the OMH filter is robust in preserving sharp edges, inhibiting edge shifting, and suppressing a wide variety of noise. The structure for the OMH filter integrated circuit is also described.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.136-138
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• 2022

에지 검출은 영상에 대하여 물체의 모양, 위치, 크기 및 재질 등과 같은 정보를 포함하고 있으며, 영상의 특징을 분석할 때 매우 중요한 요소이다. 기존의 에지 검출 방법에는 1차 미분을 이용한 소벨 필터(Sobel edge detection filter), 로버츠 필터(Roberts edge detection filter), 프리윗 필터(Prewitt edge detect ion filter) 등이 있으며, 2차 미분을 이용한 LoG(Laplacian of Gaussian) 등이 있다. 하지만 이러한 방법들은 전체 영상 영역에 대해 고정된 가중치 마스크를 적용하기 때문에 에지 검출 결과가 다소 미흡하다는 단점이 있다. 따라서 본 논문에서는 마스크 내의 중심 화소를 고려하여 에지 검출 특성을 높이는 에지 검출 알고리즘을 제안한다. 또한 제안한 에지 검출 성능을 확인하기 위하여 시뮬레이션 결과 영상을 통해 비교하였다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.437-440
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• 2022

현대 사회는 4차 산업 혁명과 IoT 기술 등의 발전으로 영상 처리 분야의 활용이 급증하고 있다. 특히, 에지 검출은 이미지 분류, 객체 검출 등 영상 처리 응용에서 필수적인 전처리 과정으로 여러 분야에서 널리 사용되고 있다. 에지를 검출하기 위한 기존의 방법에는 소벨 필터(Sobel edge detection filter), 로버츠 필터(Roberts edge detection filter), 프리윗 필터(Prewitt edge detection filter), LoG(Laplacian of Gaussian) 등이 있다. 하지만 기존의 방법들은 명암도가 낮은 저조도 환경에서 에지 검출 특성이 다소 미흡한 성능을 보인다는 단점이 있다. 따라서 본 논문에서는 저조도 환경에서도 에지 검출 특성을 높이기 위해 명암도 분석에 기반한 에지 검출 알고리즘을 제안한다.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권3호
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• pp.173-180
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• 2003

The modified Wiener filtering method is proposed for effective noise suppression in edge region of images corrupted by additive white gaussian noise. Although the pixels classified as a edge region in the conventional Wiener filter have lots of noise components, the conventional Wiener filler cannot remove noise effectively due to the preserving of edges. To reduce noise well in edge region, we modify filter coefficients of the conventional Wiener filter The modified filter coefficients increase in noise suppression effect In edge region, while they preserve edges for strong edge region. From simulation $(256{\time}256$ size, 256 graylevel images) filtered images by the proposed method show much improved subjective image quality with some improved peak signal-to-noise ratio compared to those by the conventional Wiener filtering.

• 방송공학회논문지
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• 제16권3호
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• pp.501-509
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• 2011

In this paper, we propose an edge profile adaptive bi-directional diffusion interpolation method which consists of shock filter and level set. In recent years many interpolation methods have been proposed but all methods have some degrees of artifacts such as blurring and jaggies. To solve these problems, we adaptively apply shock filter and level set method where shock filter enhances edge along the normal direction and level set method removes jaggies artifact along the tangent direction. After the initial interpolation, weights of shock filter and level set are locally adjusted according to the edge profile. By adaptive coupling shock filter with level set method, the proposed method can remove jaggies artifact and enhance the edge. Experimental results show that the average PSNR and MSSIM of our method are increased, and contour smoothness and edge sharpness are also improved.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권2호
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• pp.118-126
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• 2004

The modified Wiener filtering method is proposed for effective noise suppression in edge region of images corrupted by additive white gaussian noise. Although the pixels classified as a edge region in the conventional Wiener filter have lots of noise components, the conventional Wiener filter cannot remove noise effectively due to the preserving of edges. To reduce noise well in edge region, we modify filter coefficients of the conventional Wiener filter. The modified filter coefficients increase in noise suppression effect in edge region, while they preserve edges for strong edge region. From simulation (256${\times}$256 size, 256 graylevel images) filtered images by the proposed method show much improved subjective image quality with higher peak signal-to-noise ratio compared to those by the conventional Wiener filtering.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.27-33
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• 2018

In this paper, we propose an efficient edge preserving smoothing filter for Infrared image that can reduce noise while preserving edge information. Infrared images suffer from low signal-to-noise ratio, low edge detail information and low contrast. So, detail enhancement and noise reduction play crucial roles in infrared image processing. We first apply a guided image filter as a local analysis. After the filtering process, we optimization globally using relativity of guided image filter. Our method outperforms the previous methods in removing the noise while preserving edge information and detail enhancement.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.236-239
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• 1995

It is useful to simulate the human visual function for the purpose of image-processing. In this study, the hardware of the spatial filter with the sensitivity of lateral inhibition is realized by the combination of optical parts with electronic circuits. The diffused film with the characteristics of Gaussian type is prepared as a spatial filter. An object's image is convoluted with the spatial filter. From the difference of the convoluted images, the zero-cross position is detected at video rate. The edge of object is extracted in real-time by the use of this equipment. The resolution of edge changes with the value of the standard deviation of diffused film. In addition, it is possible to extract a directional edge selectively when the spatial filter with directional selectivity is used instead of Gaussian type of spatial filter.

• 대한원격탐사학회지
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• 제36권4호
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• pp.503-513
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• 2020

This paper presents a method to evaluate denoising filters based on edge locations in their denoised images. Image quality assessment has often been performed by using structural similarity (SSIM). However, SSIM does not provide clearly the geometric accuracy of features in denoised images. Thus, in this paper, a method to localize edge locations with subpixel accuracy based on adaptive weighting of gradients is used for obtaining the subpixel locations of edges in ground truth image, noisy images, and denoised images. Then, this paper proposes a method to evaluate the geometric accuracy of edge locations based on root mean squares error (RMSE) and jaggedness with reference to ground truth locations. Jaggedness is a measure proposed in this study to measure the stability of the distribution of edge locations. Tested denoising filters are anisotropic diffusion (AF), bilateral filter, guided filter, weighted guided filter, weighted mean of patches filter, and smoothing filter (SF). SF is a simple filter that smooths images by applying a Gaussian blurring to a noisy image. Experiments were performed with a set of simulated images and natural images. The experimental results show that AF and SF recovered edge locations more accurately than the other tested filters in terms of SSIM, RMSE, and jaggedness and that SF produced better results than AF in terms of jaggedness.