• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.9-16
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• 2018

Images with lower illumination from the light source or with dark regions due to shadows, etc., can improve subjective image quality by using retinex-based image enhancement schemes. The retinex theory is a method that recognizes the relative lightness of a scene, rather than recognizing the brightness of the scene. The way the human visual system recognizes a scene in a specific position can be in one of several methods: single-scale retinex, multi-scale retinex, and multi-scale retinex with color restoration (MSRCR). The proposed method is based on the MSRCR method, which includes a color restoration step, which consists of three phases. In the first phase, the existing MSRCR method is applied. In the second phase, the dynamic range of the MSRCR output is adjusted according to its histogram. In the last phase, the proposed method transforms the retinex output value into the display dynamic range using a logarithm transformation function considering human visual system characteristics. Experimental results show that the proposed algorithm effectively increases the subjective image quality, not only in dark images but also in images including both bright and dark areas. Especially in a low lightness image, the proposed algorithm showed higher performance improvement than the conventional approaches.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.108-116
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• 2018

In this paper, we propose an image enhancement method based on Multi-Scale Retinex theory that designs Unsharp Masking Filter (UMF) and emphasizes the contrast ratio adaptively. Unsharp Masking (UM) technique emphasizes image sharpness and improves contrast ratio by adding high frequency component to the original image. The high frequency component is obtained by differentiating between original image and low frequency image. In this paper, we present how to design an UMF kernel and to adaptively apply it to increase the contrast ratio according to multi-scale retinex theory which resembles human visual system. Experimental results show that the proposed method has better quantitative performance indexes such as PSNR, ambe & SSIM and better qualitative feature like halo artifact suppression.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.417-420
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• 2011

로봇이 능동적으로 행동하기 위해서는 외부 신호를 받아서 처리해야 되는데 여러 가지 센서 중에서도 영상처리가 중요해지고 있다. 하지만 영상처리를 사용하였을 경우는 예측할 수 없는 외부환경으로부터 영향을 받을 수 있다. 예를 들면 조명이 일정한 내부 환경에서는 인식이 가능하나 외부환경에서는 불가능한 경우가 있다. 그러므로 로봇산업이 발전에 중요한 축을 담당하고 있는 영상처리에 분야 중에서 논문에서는 조명이 변하는 상황을 설정해보고 그 상황을 토대로 기존의 알고리즘인 [2][3] Single-scale Retinex. [4][5] Multi-scale Retinex와 인식률을 비교해보고 Single-scale Retinex을 기반으로 단순히 Multi- scale Retinex처럼 가중치를 같이 두는 것이 아니라 상황에 따라 가중치를 다르게 주는 알고리즘 'Adaptive Two Scale Relinex Algorilhm'을 소개하겠다. 더불어 앞으로 나아가야 될 방향에 대해서도 언급하겠다.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1546-1549
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• 2009

In recent studies on tone reproduction with the objective of reproducing natural looking colors in digital images, an integrated multi-scale retinex (IMSR) has produced great naturalness in the resulting images. Most methods, including IMSR, work in RGB or quasi-RGB color spaces. As such, this produces hue distortion from the perspective of the human visual system. Accordingly, this paper proposes the hue preserved multi-scale Retinex (HPMSR) method to obtain a high contrast and naturalness. The proposed method enhanced the $L^*$ and saturation values in CIELAB color space. As a result, the visibility in dark shadows in the resulting images was improved.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1481-1490
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• 2017

This paper presents an image quality enhancement algorithm for dark image acquired under poor lighting condition. Various retinex algorithms which are human perception-based image processing methods were proposed to solve this problem. Although MSR(Multi-Scale Retinex) among these algorithm works well under most lighting condition, it shows color degradation because their separate nonlinear processing of RGB color channels. To compensate for the loss of the color, MSRCR(Multi-Scale Retinex with Color Restoration) was proposed. However, it requires high computational load and has additional parameters that need to be adjusted according to input image. In order to overcome this problem, a new retinex algorithm based on MSR is proposed in this paper. The proposed method consists of V channel MSR, saturation correction, and separate contrast enhancement process. Experimental results show that the subjective and objective image quality of the proposed method better than those of the conventional methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.99-107
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• 2019

Retinex-based image enhancement is a technique that utilizes the property that the human visual characteristics are sensitive to the difference from the surrounding pixel value rather than the pixel value itself. These Retinex-based algorithms show different characteristics of the improved image depending on the applied color space or gamma correction. In this paper, we set eight different experimental conditions according to the application of color space and gamma correction, and analyze the objective and subjective performance of each Retinex based image enhancement algorithm and apply it to the implementation of Retinex based algorithm. In the case of gamma correction, quantitative low entropy images and low contrast images are obtained. The application of Retinex technique in HSI color space rather than RGB color space is found to be high in overall subjective image quality as well as maintaining color.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.936-941
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• 2006

To apply object recognition techniques to real environment, illumination compensation method should be developed. As effective illumination compensation model, we focused our attention on Retinex model and illumination-Reflectance model, implemented them, and experimented on their performance. We implemented Retinex model with Single Scale Retinex, Multi-Scale Retinex, and Retinex Neural Network and Multi-Scale Retinex Neural Network, neural network model of Retinex model. Also, we implemented illumination-Reflectance model with reflectance image calculation by calculating an illumination image by low frequency filtering in frequency domain of Discrete Cosine Transform and Wavelet Transform, and Gaussian blurring. We compare their illumination compensation performance to facial images under nine illumination directions. We also compare their performance after post processing using Principal Component Analysis(PCA). As a result, illumination Reflectance model showed better performance and their overall performance was improved when illumination compensated images were post processed by PCA.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.727-728
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• 2006

In this paper, we analyzed the color restoration method to be used in the Multi-Scale Retinex and proposed new color restoration method. and, we compared the result of the algorithm to be proposed with conventional algorithm through the experiment.

• Journal of Korea Multimedia Society
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• v.13 no.11
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• pp.1621-1629
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• 2010

This paper presents an improved fast MSRCR algorithm that MSRs are commonly adopted at tone mapping in color vision. Conventional MSRs consist of three SSRs, which use three Gaussian functions with different scales as those surround ones. This convolution processes require much computation load. Therefore, the proposed algorithm adopts a hierarchical discrete correlation which is equivalent to Gaussian function and the Retinex process is only applied to the luminance channel in order to get a fast processing. A simple color preservation scheme is applied to the Retinex output from the luminance channel in the proposed MSRCR algorithm. Experimental results show that the proposed algorithm required less number of oprations and computation time about 1/9.5 and 1/3.5 times, respectively, than those of the simplest MSR and was equivalent to conventional MSRs.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.87-90
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• 2018

Various approaches have been proposed to convert low dynamic range (LDR) to high dynamic range (HDR). Of these approaches, the Multi Scale Fusion (MSF) algorithm based on Laplacian pyramid decomposition is used in many applications and demonstrates its usefulness. However, the pyramid fusion technique has no means for controlling the luminance component because the total number of pixels decreases as the pyramid rises to the upper layer. In this paper, we extract the reflection light of the image based on the Retinex theory and generate the weight map by adjusting the reflection component. This weighting map is applied to achieve an MSF-like effect during image fusion and provides an opportunity to control the brightness components. Experimental results show that the proposed method maintains the total number of pixels and exhibits similar effects to the conventional method.