An L&lt;inf&gt;1&lt;/inf&gt;-based variational model for Retinex theory and its application to medical images

Ma, Wenye; Morel, Jean‐Michel; Osher, Stanley; Chien, Aichi

doi:10.1109/cvpr.2011.5995422

Cited by 69 publications

(61 citation statements)

References 19 publications

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“…Ma et al performed the variational optimization by minimizing the constraint term on the reflectance component using l 1 -norm [18]. Fu et al proposed the bright channel prior (BCP) to reduce the halo effect and color distortion using l 2 -norm minimization on the illumination and reflectance components [19].…”

Section: Introductionmentioning

confidence: 99%

Low-light image restoration using bright channel prior-based variational Retinex model

Park

Moon

et al. 2017

J Image Video Proc.

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This paper presents a low-light image restoration method based on the variational Retinex model using the bright channel prior (BCP) and total-variation minimization. The proposed method first estimates the bright channel to control the amount of brightness enhancement. Next, the variational Retinex-based energy function is iteratively minimized to estimate the improved illumination and reflectance using the BCP. Contrast of the estimated illumination is enhanced using the gamma correction and histogram equalization to reduce a color distortion and noise amplification. Experimental results show that the proposed method can provide the better restored result than the existing methods without unnatural artifacts such as noise amplification and halo effects near edges.

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Section: Introductionmentioning

confidence: 99%

Low-light image restoration using bright channel prior-based variational Retinex model

Park

Moon

et al. 2017

J Image Video Proc.

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“…According to their implementation forms, the standard Retinex algorithms can be roughly categorized as path-based algorithms, 15,17,18 iterative algorithms, 11,19,20 center/surround algorithms, 21,22 and PDE-type algorithms. [23][24][25][26] Together with many extensions for other purposes, the huge family of Retinex-type algorithms are now widely applied in many aspects of image processing, such as color constancy, 17,18,20,25 general image enhancement, 5,21,27 HDR image rendering, [28][29][30][31] and shadow removal, 32-34 among others.…”

Section: Retinex Theory and Its Variantsmentioning

confidence: 99%

Adapting iterative retinex computation for high-dynamic-range tone mapping

Pan

2013

J. Electron. Imaging

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Abstract. Retinex algorithms have been widely applied in many aspects of image processing. Based on the iterative Retinex algorithm, we propose an edge-preserving illumination estimation method. Inspired by the anisotropic diffusion, an edge-stopping function is introduced in the iterative computation. This modification enables the preservation of abrupt edges when computing the upper envelope of a given image. Based on the illumination-reflectance decomposition, a high-dynamic-range (HDR) radiance map can be easily tone-mapped to be a low-dynamic-range image by compressing the range of the estimated illumination. Artifacts are effectively suppressed using the proposed method. Meanwhile, we also propose a jumping-spiral iteration manner to improve the symmetry of the edge response. Experimental results show that the proposed tone mapping algorithm is very effective in reproducing HDR scenes, and has a better performance compared with some similar operators. IntroductionThe dynamic range of natural scenes varies accordingly with different lighting conditions, from lower than 40 dB for ordinary uniformly lit scenes to higher than 120 dB for mixed indoor-outdoor scenes. However, human observers can cope well with such tremendous difference through local adaptation embedded in retina and cortex. Powered by sophisticated mechanisms, the human visual systems (HVS) can perceive a very large intensity range simultaneously, and even larger after long-time adaptation.1,2 On the other hand, the dynamic range of current image displays and other reproduction media is rather limited. Consequently, there is a great demand for high-fidelity dynamic range reduction in many media-related applications, such as remote imaging, medical imaging, virtual reality, and digital photography.

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“…It is difficult to set up existence results for their models. Ma et al further proposed a 1 -based variational model to recover the reflection component [25]. In [26], Zosso et al proposed a unifying framework for Retinex theory.…”

Section: Introductionmentioning

confidence: 99%

A Convex Optimization Model and Algorithm for Retinex

Zhao

Huang

Zhao

et al. 2017

Mathematical Problems in Engineering

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Retinex is a theory on simulating and explaining how human visual system perceives colors under different illumination conditions. The main contribution of this paper is to put forward a new convex optimization model for Retinex. Different from existing methods, the main idea is to rewrite a multiplicative form such that the illumination variable and the reflection variable are decoupled in spatial domain. The resulting objective function involves three terms including the Tikhonov regularization of the illumination component, the total variation regularization of the reciprocal of the reflection component, and the data-fitting term among the input image, the illumination component, and the reciprocal of the reflection component. We develop an alternating direction method of multipliers (ADMM) to solve the convex optimization model. Numerical experiments demonstrate the advantages of the proposed model which can decompose an image into the illumination and the reflection components.

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An L<inf>1</inf>-based variational model for Retinex theory and its application to medical images

Cited by 69 publications

References 19 publications

Low-light image restoration using bright channel prior-based variational Retinex model

Low-light image restoration using bright channel prior-based variational Retinex model

Adapting iterative retinex computation for high-dynamic-range tone mapping

A Convex Optimization Model and Algorithm for Retinex

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