A Convex Optimization Model and Algorithm for Retinex

Zhao, Qingnan; Huang, Ting‐Zhu; Zhao, Xi-Le; Ma, Tianhui; Cheng, Minghui

doi:10.1155/2017/4012767

Cited by 3 publications

(1 citation statement)

References 44 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, low-light image enhancement can be seen as a procedure for adjusting illumination component independent reflectance, because it aims to improve the brightness to acquire normal light images and the reflectance represents the original intrinsic appearance of the scene objects. However, directly decomposing an input image into reflectance and illumination components [28][29][30][31] is an ill-posed problem that could induce unrealistic results to some extent [32].…”

Section: The Proposed Methodsmentioning

confidence: 99%

Low‐light image enhancement based on Retinex decomposition and adaptive gamma correction

Yang

Yue

et al. 2020

IET Image Processing

View full text Add to dashboard Cite

Low-light images suffer from poor visibility and noise. In this paper, a low-light image enhancement method based on Retinex decomposition is proposed. A pyramid network is first utilized to extract multi-scale features to improve the quality of Retinex decomposition. Then the decomposed illumination is refined via an adaptive Gamma correction network to handle non-uniform illumination, while the decomposed reflectance is refined with a lightweight network. Finally, the enhanced image is obtained by element-wise multiplication between the refined illumination and reflectance components. Quantitative and qualitative experiments demonstrate the superiority of our method over state-of-the-art image enhancement methods. 1 INTRODUCTION Low-light images are usually degraded by low contrast and noise, which impede many computer vision tasks, such as tracking and detection. Therefore, improving the quality of lowlight images remains an essential task in computer vision. The main task of low-light images enhancement methods is to create visual pleasing and informative images, which are of high contrast and noise-free. There are many attempts to enhance low-light images, such as the histogram equalisation (HE) based methods [1-4], dehazing based methods [5, 6], Retinex decomposition based methods [7-9], and learning based methods [10-12]. Histogram equalisation(HE) [1] is one of the most simplest and widely utilized methods for image enhancement. Local histogram equalisation(LHE) [3] firstly utilize a predefined window to perform the histogram equalisation locally. Another variation [2] combines global and local hierarchical levels of HE by incorporating local features around a neighborhood for each pixel to aid equalisation. These two variation methods improve performance by sacrificing computational complexity. Jung et al. [4] present a pixel mapping function based on probability through HE, which could produce pleasing results in large smooth regions; however, it is ineffective to restore sharpness at the edges. It should be mentioned that the HE-based methods This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract