• Journal of the Optical Society of Korea
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• v.18 no.6
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• pp.809-817
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• 2014

The loss in contrast-discrimination ability of the human visual system under high ambient illumination level can cause image quality degradation in mobile phones. In this paper, we propose a perceived contrast compensation method by processing the original displayed image. With consideration that the perceived contrast significantly varies across the image, this method extracts the local band contrast from the original image; it then compensates these contrast components to counteract the perceived contrast degradation. Experimental results demonstrate that this method can maintain most contrast details even in high ambient illumination levels.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.104-111
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• 2010

This paper presents an adaptive image enhancement method using the spatial frequency property in the DCT(discrete cosine transform) compressed domain. The dc coefficients, the illumination components of image, are adjusted to compress the dynamic range of image, and the ac coefficients are modified to enhance the contrast by using the human visual system(HVS) and the spatial frequency property. The ac coefficients are separated into vertical direction, horizontal direction, and mixed spatial frequency components, and adaptively modified to minimize the block artifacts that possibly occur in the image enhancement. The proposed method using dynamic range compression and adaptive contrast enhancement shows the advanced performance without the block artifact compared with existing method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1378-1387
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• 2013

Histogram equalization, which stretches the dynamic range of intensity, is the most common method for enhancing the contrast of image. Contrast limited adaptive histogram equalization(CLAHE), proposed by K. Zuierveld, has two key parameters: block size and clip limit. These parameters mainly control image quality, but have been heuristically determined by user. In this paper, we propose a novel method of determining two parameters of CLAHE using entropy of image. The key idea is based on the characteristics of entropy curves: clip limit vs entropy and block size vs entropy. Clip limit and block size are determined at the point with maximum curvature on entropy curve. Experimental results show that the proposed method improves images with very low contrast.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.122-125
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• 2003

An adaptive contour smoothing algorithm designed as a preprocessor for shape coders is presented. In the proposed method, the degree of the adaptive smoothing is controlled based on the significance of each contour point, which is quantified according to inter-region contrast in an intensity image. The actual smoothing consists of an expansion operator and a thinning algorithm. Experimental results show that the proposed method results in a saving of about 20% in number of coded bits with a negligible additional texture degradation in the reconstructed intensity image.

• Journal of Multimedia Information System
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• v.4 no.2
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• pp.65-73
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• 2017

This paper proposes a method to improve the image quality by designing Unsharp Mask Filter (UMF) based on Retinex theory which controls the frequency pass characteristics adaptively. Conventional unsharp masking technique uses blurring image to emphasize sharpness of image. Unsharp Masking(UM) adjusts the original image and sigma to obtain a high frequency component to be emphasized by the difference between the blurred image and the high frequency component to the original image, thereby improving the contrast ratio of the image. In this paper, we design a Unsharp Mask Filter(UMF) that can process the contrast ratio improvement method of Unsharp Masking(UM) technique with one filtering. We adaptively process the contrast ratio improvement using Unsharp Mask Filter(UMF). We propose a method based on Retinex theory for adaptive processing. For adaptive filtering, we control the weights of Unsharp Mask Filter(UMF) based on the human visual system and output more effective results.

• Journal of Digital Contents Society
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• v.5 no.1
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• pp.79-86
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• 2004

In this paper we implemented the Real Time Contrast Enhancer for image quality enhancement of moving picture. Also we proposed adaptive contrast method that use mean and variance of input video signal. The Designed the contrast Enhancer is measured in comparison with conventional picture and interfaced to 30inch TFT LCD TV of the LG Electronics.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.51-57
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• 2005

In this paper, we propose an adaptive contrast control method for the flat real-time processing. The proposed method has employed probability density function(PDF) in order to control a sudden change in image-brightness. In addition, the proposed algerian obtains the maximum contrast without affecting the processed image. In order to reduce hardware complexity, we have utilized approximated CDF based on sampling values. Visual test and standard deviation of their histogram have been introduced to evaluate the resultant output images of at: proposed method and the original ones.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.6
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• pp.358-365
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• 2014

This paper proposes a method that reduces the flickering effect caused by A-GLG (Adaptive Gray-Level Grouping) during video contrast enhancement. Of the GLG series, A-GLG shows the best contrast enhancement performance. The GLG series is based on histogram grouping. Histogram grouping is calculated differently between the continuous frames with a similar histogram and causes a subtle change in the transformation function. This is the reason for flickering effect when the video contrast is enhanced by A-GLG. To reduce the flickering effect caused by A-GLG, the proposed method calculates a modified transformation function. The modified transformation function is calculated using a previous and current transformation function applied with a weight separately. The proposed method was compared with A-GLG for flickering effect reduction and video contrast enhancement. Through the experimental results, the proposed method showed not only a reduced flickering effect, but also video contrast enhancement.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.263-270
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• 2009

Histogram Equalization (HE) is a very popular technique for enhancing the contrast of an image. HE stretches the dynamic range of an image using the cumulative distribution function of a given input image, therefore improving its contrast. However, HE has a well-known problem : when HE is applied for the contrast enhancement, there is a significant change in brightness. To resolve this problem, we propose An Adaptive Contrast Enhancement Algorithm using Subhistogram Area-Ratioed Histogram Redistribution, a new method that helps reduce excessive contrast enhancement. This proposed algorithm redistributes the dynamic range of an input image using its mean luminance value and the ratio of sub-histogram area. Experimental results show that by this redistribution, the significant change in brightness is reduced effectively and the output image is able to preserve the naturalness of an original image even if it has a poor histogram distribution.