• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.12
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• pp.1054-1064
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• 2012

HDR (high dynamic range) tone mapping algorithms are used in image processing that reduces the dynamic range of an image to be displayed on LDR (low dynamic range) devices properly. The retinex is one of the tone mapping algorithms to provide dynamic range compression, color constancy, and color rendition. It has been developed through multi-scale methods and luminance-based methods. Retinex algorithms still have drawbacks such as the emphasized noise and desaturation. In this paper, we propose a multi scale tone mapping algorithm for enhancement of contrast, saturation, and noise of HDR rendered images based on visual brightness functions. In the proposed algorithm, HSV color space has been used for preserving the hue and saturation of images. And the algorithm includes the estimation of minimum and maximum luminance level and a visual gamma function for the variation of viewing conditions. And subjective and objective evaluations show that proposed algorithm is better than existing algorithms. The proposed algorithm is expected to image quality enhancement in some fields that require a improvement of the dynamic range due to the changes in the viewing condition.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.2
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• pp.90-94
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• 2013

In this paper, we propose a new algorithm to improve the contrast ratio, to preserve information of bright regions and to maintain the color of backlight image that appears with a great relative contrast. Backlight images of the natural environment have characteristics for difference of local brightness; the overall image contrast improvement is not easy. To improve the contrast of the backlight images, MSR (Multi-Scale Retinex) algorithm using the existing multi-scale Gaussian filter is applied. However, existing multi-scale Gaussian filter involves color distortion and information loss of bright regions due to excessive contrast enhancement and noise because of the brightness improvement of dark regions. Moreover, it also increases computational complexity due to the use of multi-scale Gaussian filter. In order to solve these problems, a linear MSR is performed that reduces the amount of computation from the HSV color space preventing the color distortion and information loss due to excessive contrast enhancement. It can also remove the noise of the dark regions which is occurred due to the improved contrast through edge preserving filter. Through experimental evaluation of the average color difference comparison of CIELAB color space and the visual assessment, we have confirmed excellent performance of the proposed algorithm compared to conventional MSR algorithm.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1365-1372
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• 2011

This paper proposes a cognitive contrast enhancement algorithm based on the non-linear masking to advance low cognitive contrast in dark regions of images. In order to improve brightness in dark regions of an image, we propose a new contrast enhancement algorithm based on the non-linear masking using regional adaptive parameters of an image. For performance evaluation of the proposed method, chromaticity and saturation comparison as a quantitative assessment and z-score comparison as a qualitative assessment were executed between test images and their simulated images by SSR, MSR, a conventional non-linear masking and the proposed method, respectively. As a result, the proposed method showed low chromaticity and saturation difference and improved cognitive contrast for the three methods.

• 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 Korea Multimedia Society
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• v.25 no.10
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• pp.1395-1403
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• 2022

Lately, over 4K high definition and high dynamic range (HDR) display devices are popularized, various interpolation and HDR methods have been researched to expand the size and the dynamic range. Since most of the legacy low resolution (LR) images require both an interpolation and a HDR tone mapping methods, the two processes should be subsequently applied. Therefore, the proposed algorithm presents a HDR up-scaling algorithm using undecimated wavelet transform and Retinex method, which transfers a LR image of low dynamic range (LDR) into the high resolution (HR) with HDR. The proposed algorithm consists of an up-scaling scheme increasing the image size and a tone mapping scheme expanding the dynamic range. The up-scaling scheme uses the undecimated version of the simplest Haar wavelet analysis for the 8-directional interpolation and the change region is extracted during the analysis. This region information is utilized in controlling the surround functions' size of the proposed tone mapping using MSRCR, to enhance the pixels of around the edges that are dominant feature of the subjective image quality. As the results, the proposed algorithm can apply an up-scaling and tone mapping processes in accordance with the type of pixel.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.385-394
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• 2009

Face tracking and recognition are difficult problems because the face is a non-rigid object. The main reasons for the failure to track and recognize the faces are the changes of a face pose and environmental illumination. To solve these problems, we propose a nonlinear manifold framework for the face pose and the face illumination normalization processing. Specifically, to track and recognize a face on the video that has various pose variations, we approximate a face pose density to single Gaussian density by PCA(Principle Component Analysis) using images sampled from training video sequences and then construct the GMM(Gaussian Mixture Model) for each person. To solve the illumination problem for the face tracking and recognition, we decompose the face images into the reflectance and the illuminance using the SSR(Single Scale Retinex) model. To obtain the normalized reflectance, the reflectance is rescaled by histogram equalization on the defined range. We newly approximate the illuminance by the trained manifold since the illuminance has almost variations by illumination. By combining these two features into our manifold framework, we derived the efficient face tracking and recognition results on indoor and outdoor video. To improve the video based tracking results, we update the weights of each face pose density at each frame by the tracking result at the previous frame using EM algorithm. Our experimental results show that our method is more efficient than other methods.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.955-956
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• 2008

This paper presents a new method for illumination normalization in eye detection. Based on the retinex image formation model, we employ the discrete wavelet transform to remove the lighting effect in face image data. The final result based on the proposed method shows the better performance in detecting eyes compared with previous work.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.49-55
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• 2010

Cast shadow caused by an illumination condition can produce troublesome effects for face recognition system using reflectance image. Consequently, we need to separate cast shadow area from feature area for improvement of recognition accuracy. A Bilateral filter smooths image while preserving edges, by means of a nonlinear combination of nearby pixel values. Processing such characteristics, this method is suited to our purpose in illumination estimation process based on Retinex. Therefore, in this paper, we propose a new illumination normalization method based on the Bilateral filter in face images. The proposed method produces a reflectance image that is preserved relatively exact cast shadow area, because coefficient of filter is designed to multiply proximity and discontinuity of pixels in input image. Performance of our method is measured by a recognition accuracy of principle component analysis(PCA) and evaluated to compare with other conventional illumination normalization methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.1
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• pp.25-44
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• 2010

With the increasing popularity of mobile devices, it has become necessary to protect private information and content in these devices. Face recognition has been favored over conventional passwords or security keys, because it can be easily implemented using a built-in camera, while providing user convenience. However, because mobile devices can be used both indoors and outdoors, there can be many illumination changes, which can reduce the accuracy of face recognition. Therefore, we propose a new face recognition method on a mobile device robust to illumination variations. This research makes the following four original contributions. First, we compared the performance of face recognition with illumination variations on mobile devices for several illumination normalization procedures suitable for mobile devices with low processing power. These include the Retinex filter, histogram equalization and histogram stretching. Second, we compared the performance for global and local methods of face recognition such as PCA (Principal Component Analysis), LNMF (Local Non-negative Matrix Factorization) and LBP (Local Binary Pattern) using an integer-based kernel suitable for mobile devices having low processing power. Third, the characteristics of each method according to the illumination va iations are analyzed. Fourth, we use two matching scores for several methods of illumination normalization, Retinex and histogram stretching, which show the best and $2^{nd}$ best performances, respectively. These are used as the inputs of an SVM (Support Vector Machine) classifier, which can increase the accuracy of face recognition. Experimental results with two databases (data collected by a mobile device and the AR database) showed that the accuracy of face recognition achieved by the proposed method was superior to that of other methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10C
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• pp.621-628
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• 2011

The skin color has been used as important cues in the systems for detecting or recognizmg the face. However, the color difference in images under different illuminants makes it difficult to find out the skin in these systems. For solving the problem, this paper proposes a method of recovering skin colors based on well-known color constancy approaches, such as Retinex, Gray World, White Patch, and Simplified Horn. To acquire experimental images under the colored scene illumination, the effects of colored illuminants were added to source images. Next, result images, having the corrected skin color by the constancy methods, were derived from the source images. The experiment results showed that most of the skin colors in our experiments were recovered into some steady range in the color space, and that Gray World had higher performance than the other methods compared.