• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.6
• /
• pp.722-728
• /
• 2020

Digital image processing has been applied in a wide range of fields due to the development of IoT technology and plays an important role in data processing. Various techniques have been proposed to remove such noise, but the conventional impulse noise canceling methods are insufficient to remove noise of edge components of an image, and have a disadvantage of being greatly affected by random impulse noise. Therefore, in this paper, we propose an algorithm that effectively removes edge component noise in random impulse noise environment. The proposed algorithm calculates the threshold value by determining the noise level and switches the filtering process by comparing the reference value with the input pixel value. The proposed algorithm shows good performance in the existing method, and the simulation results show that the noise is effectively removed from the edge of the image.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.44 no.5
• /
• pp.407-415
• /
• 2016

In this paper, an aviation image fusion method is proposed for creating an informative fused image through gray scale images within noise. The proposed method is based on an adaptive guided filter which adjusts regulation parameter of the filter based on peak signal noise ratio (PSNR) in order to behave as an edge-preserving filtering property. Simulation results demonstrate that the proposed method preserves the edge information of the input image and reduces the noise effect while maintaining designed PSNR.

• 한국지형공간정보학회:학술대회논문집
• /
• 2002.11a
• /
• pp.133-140
• /
• 2002

This paper presents a method for line segment grouping used for detection of various building roofs. First, by using edge preserving filtering. noise is eliminated and then images are segmented by watershed algorithm, which preserves location of edge pixels. To extract line segments between control points from boundary of each region, we calculate curvature of each pixel on the boundary and then find the control points. Line linking is performed according to direction and length of line segments and finally the location of line segments is adjusted using gradient magnitudes of all pixels of the line segment. The algorithm has been applied to aerial imagery and the results show accurate building roof detection.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.11
• /
• pp.2413-2418
• /
• 2010

We propose a retouching method that converts a photography taken by smartphone to a watercolor painting image using bilateral filtering and mean shift segmentation which are mostly used in image processing. The first step is to convert an input image to fit the screen resolution of smartphone. And next step is to weaken high frequency components of the image, while preserving the edge of image using the bilateral filtering. And after that we perform mean shift segmentation from the bilateral filtered image. We apply parameters of mean shift segmentation considering the processing speed of smartphone. Experimental result shows that our method can be applied to various types of image and bring better result.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.7
• /
• pp.89-101
• /
• 1997

A technical method of noise modeling and adaptive filtering reducing of speckle noise in ultrasonic medical images is presented. By adjusting the characteristics of the filer according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in diagnosis. Homogeneous factor(HF) from the noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the diagnostic systems with various inner statistical properties is proposed. We show by the experimented that the performance of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region.

• Journal of information and communication convergence engineering
• /
• v.6 no.1
• /
• pp.80-85
• /
• 2008

Images are often corrupted by noises during signal acquisition and transmission. Among those noises, additive white Gaussian noise (AWGN) and impulse noise are most representative. For different types of noise have different characters, how to remove them separately from degraded image is one of the most fundamental problems. Thus, a modified image restoration algorithm is proposed in this paper, which can not only remove impulse noise of random values, but also remove the AWGN selectively. The noise detection step is by calculating the intensity difference and the spatial distance between pixels in a mask. To divide two different noises, the method is based on three weighted parameters. And the weighted parameters in the filtering mask depend on spatial distances, positions of impulse noise and standard deviation of AWGN. We also use the peak signal-to-noise ratio (PSNR) to evaluate restoration performance, and simulation results demonstrate that the proposed method performs better than conventional median-type filters, in preserving edge details.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.1711-1714
• /
• 2003

The noise in an image degrades image quality and deteriorates coding efficiency of compression. Recently, various edge-preserving noise filtering methods based on the nonstationary image model have been proposed to overcome this problem. In most conventional nonstationary image models, however, pixels are assumed to be uncorrelated to each other In order not to increase the computational burden too much. As a result, some detailed information is lost in the filtered results. In this paper, we propose a computationally feasible adaptive noise smoothing algorithm which considers the nonstationary correlation characteristics of images. We assume that an image has a nonstationary mean and can be segmented into subimages which have individually different stationary correlations. Taking advantage of the special structure of the covariance matrix that results from the proposed image model, we derive a computationally efficient FFT-based adaptive linear minimum mean square error filter. The justification for the proposed image model is presented and the effectiveness of the proposed algorithm is demonstrated experimentally.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.11a
• /
• pp.699-702
• /
• 2002

본 논문에서는 anisotropic diffusion 방법의 일종인 평균곡률 확산 (Mean Curvature Diffusion) 방법을 이용하여 영상에 포함된 잡음은 제거하고 동시에 에지는 보존하는 기법을 제안한다. 평균곡률 확산은 2 차원 영상의 밝기값을 3 차원 공간상의 z 좌표에 대응시켜 영상의 밝기값에 대응하는 공간 상의 곡면을 구성하고 이 곡면을 평균곡률에 비례하는 속도로 확산시킨다. 확산이 진행되면서 평균곡률이 영이 되는 에지에서는 확산이 발생하지 않고 잡음 등의 영향이 많은 에지 이외의 영역에서는 확산이 빠른 속도로 진행된다. 기존의 평균곡률 확산 방법의 성능을 개선하기 위해 최소/최대 흐름 방법을 평균곡률 확산 방법과 결합시키고 영상의 2 차 도함수를 사용하여 d얇은 에지를 보존하였다. 실험을 통해 제안한 방법이 기존의 방법보다 잡음 제거와 에지 보존 성능이 우수함을 확인할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.10a
• /
• pp.206-208
• /
• 2010

We propose a retouching method that converts a photography taken by smartphone to a watercolor painting image using bilateral filtering and mean shift segmentation which are mostly used in image processing. The first step is to convert an input image to fit the screen resolution of smartphone. And next step is to weaken high frequency components of the image, while preserving the edge of image using the bilateral filtering. And after that we perform mean shift segmentation from the bilateral filtered image. We apply parameters of mean shift segmentation considering the processing speed of smartphone. Experimental result shows that our method can be applied to various types of image and bring better result.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.1
• /
• pp.658-665
• /
• 2015

In the production of medical images, noise reduction and contrast enhancement are important methods to increase qualities of processing results. By using the edge-based denoising and adaptive nonlinear histogram stretching, a novel medical image enhancement algorithm is proposed. First, a medical image is decomposed by wavelet transform, and then all high frequency sub-images are decomposed by Haar transform. At the same time, edge detection with Sobel operator is performed. Second, noises in all high frequency sub-images are reduced by edge-based soft-threshold method. Third, high frequency coefficients are further enhanced by adaptive weight values in different sub-images. Finally, an adaptive nonlinear histogram stretching method is applied to increase the contrast of resultant image. Experimental results show that the proposed algorithm can enhance a low contrast medical image while preserving edges effectively without blurring the details.