• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.918-920
• /
• 2011

In this paper, a simple edge pattern classification method is proposed for detecting straight line segments in an image corrupted by impulse noise. Corrupted images have complicated edge patterns. To detect straight line from an complicated edge pattern, it is needed to simplify the entire edge. The proposed algorithm separates the entire edge into 4 directional partial edge patterns. Each line segment is separated from the partial edge image where several line segments are overlapped, and then the straight line is detected. The results of the experiments emphasize that the proposed algorithm is simple but accurate.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.2C
• /
• pp.124-131
• /
• 2007

This paper presents a block loss recovery technique for the image block data corrupted by transmission losses through the employment of fine directional interpolation (FDI). The proposed algorithm introduces a spatial direction vector (SDV). The SDVs are extracted from the edge information of the neighboring image data. Subsequently, the SDVs are adaptively applied to interpolate lost pixels on a pixel-by-pixel basis. This approach improves the capability to more reliably recover high-detailed contents in the corrupted block. Experimental results demonstrate that the FDI method performs better as compared to previous techniques.

• Journal of information and communication convergence engineering
• /
• v.9 no.5
• /
• pp.591-596
• /
• 2011

Image denoising is a key issue in all image processing researches. Generally, the quality of an image could be corrupted by a lot of noise due to the undesired conditions of image acquisition phase or during the transmission. Many approaches to image restoration are aimed at removing either Gaussian or impulse noise. Nevertheless, it is possible to find them operating on the same image, which is called mixed noise and it produces a hard damage. In this paper, we proposed noise type classification method and a mixed nonlinear filter for mixed noise suppression. The proposed filtering scheme applies a modified adaptive switching median filter to impulse noise suppression and an efficient nonlinear filer was carried out to remove Gaussian noise. The simulation results based on Matlab show that the proposed method can remove mixed Gaussian and impulse noise efficiently and it can preserve the integrity of edge and keep the detailed information.

• Journal of information and communication convergence engineering
• /
• v.10 no.1
• /
• pp.45-52
• /
• 2012

Thedenoising of a natural image corrupted by Gaussian noise is a long established problem in signal or image processing. Today the research is focus on the wavelet domain, especially using the wavelet threshold method. In this paper, a waveletbased image denoising modified adaptive thresholding method is proposed. The proposed method computes thethreshold adaptively based on the scale level and adaptively estimates wavelet coefficients by using a modified thresholding function that considers the dependency between the parent coefficient and child coefficient and the soft thresholding function at different scales. Experimental results show that the proposed method provides high peak signal-to-noise ratio results and preserves the detailed information of the original image well, resulting in a superior quality image.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.480-485
• /
• 2005

Perspective dynamical systems arise in machine vision problems, in which only perspective observation is available. This paper considers the state estimation problem for a rigid body moving in three dimensional spaces using the image data obtained by a CCD camera or some other means. Because the motion of the rigid body and the observed data are generally corrupted by noises, it is necessary to seek a state estimation method to reduce the influence of the noises. In this paper, by means of computer simulations for a simple example, we examine the sensitivity to the noises of the nonlinear observer developed in the recent paper ([1] R. Abdursul, H. Inaba and B. Ghosh, Nonlinear observers for perspective time-invariant linear systems, Automatica, vol. 40, Issue 3, pp. 481-490, 2004) and the effectiveness of the Extended Kalman Filter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.10a
• /
• pp.367-370
• /
• 2007

Both impluse noise and AWGN (additive white Gaussian noise) are easily corrupted into images, during signal transmission and acquisition. Thus, an algorithm for removing both noises is represented in this paper. An impulse noise detection step can effectively separate impulse noise with AWGN, then in the noise filtering step, by using several parameters, not only impulse noise but also AWGN can be reduced. The value of those parameters are automatically changeable when the standard deviation of AWGN, the impulse noise density, and the spatial distances between pixels are different. Results of computer simulations show that the proposed approach performs better than other conventional filters.

• Journal of information and communication convergence engineering
• /
• v.9 no.1
• /
• pp.59-63
• /
• 2011

Conventional noise reduction filtering schemes realize limited improvements of the peak signal-to-noise ratio (PSNR) in the low-level noisy images. The flatness degree and the edge information are effectively used to estimate the noise volume. We propose a noise estimator for reducing noise in the AWGN (additive white gaussian noise) corrupted images using three intermediate image maps (FGM(flatness gray map), FIM(flatness index map), NEM(noise estimate map)). The proposed noise estimator is fed into the conventional noise reduction filters as a pre-processor. The performance of noise reduction is tested in the various AWGN corrupted images.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.5
• /
• pp.103-108
• /
• 1993

An image can be largely corrupted by the ambient illuminant, so that the image enhancement to restory natural color without respect to the ambient illuminant is needed. It this paper, a new color image enhancement technique based on color constancy is proposed. To enhance the image quality, higher volues of contrast and saturation are preferred, but their excessive values make an image unnatural. Since the color constancy processing preserves only hue, while reducing the dynamic range of lightness and saturation,the technique is needed in order to compensate this phenomenon. The proposed method transforms and increases lightness and saturation simultaneously to avoid the complexity in the related transformation by analyzing the relationship between the RGB and modified IHS coordinate system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.8
• /
• pp.3842-3855
• /
• 2018

A novel image completion method is proposed that uses the advantage of planar structural information to fill corrupted portions of an image. First, in estimating parameters of the projection plane, the image is divided into several planes, and their planar structural information is analyzed. Second, in calculating the a priori probability of patch and patch offset regularity, this information is converted into a constraint condition to guide the process of filling the hole. Experimental results show that the proposed algorithm is fast and effective, and ensures the structure continuity of the damaged region and smoothness of the texture.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.7
• /
• pp.1373-1379
• /
• 2007

A modified adaptive switching median filter for impulse noise removal, which has the noise detection step and the noise filtering step, is proposed in this paper. In the noise detection step, we use the detection threshold which is earned by calculating the intensity differences between pixels nearby with each other in localized window, to determine whether the pixels in the image are noise or not. Then in the noise filtering step, we will only remove the corrupted pixels and remain the good pixels. By the noise detection result, we can easily get the local noise density of the image, and use it to consider the filtering mask size and the times of filtering iteration according to different localized noise corruptions. For Setting the simulation result, we compared the proposed method to conventional median filters with several test images corrupted by various impulse noise densities. We also use the peak signal-to-noise ratio (PSNR) to evaluate restoration performance, the simulation results demonstrate that the proposed method shows better results than other median-based type filters.