LR3M: Robust Low-Light Enhancement via Low-Rank Regularized Retinex Model

By Xutong Ren, Wenhan Yang, Wen-Huang Cheng, Jiaying Liu. IEEE Transactions on Image Processing, 2020

363 citations recorded by the source corpus.

Noise causes unpleasant visual effects in low-light image/video enhancement. In this paper, we aim to make the enhancement model and method aware of noise in the whole process. To deal with heavy noise which is not handled in previous methods, we introduce a robust low-light enhancement approach, aiming at well enhancing low-light images/videos and suppressing intensive noise jointly. Our method is based on the proposed Low-Rank Regularized Retinex Model (LR3M), which is the first to inject low-rank prior into a Retinex decomposition process to suppress noise in the reflectance map. Our method estimates a piece-wise smoothed illumination and a noise-suppressed reflectance sequentially, avoiding remaining noise in the illumination and reflectance maps which are usually presented in alternative decomposition methods. After getting the estimated illumination and reflectance, we adjust the illumination layer and generate our enhancement result. Furthermore, we apply our LR3M to video low-light enhancement. We consider inter-frame coherence of illumination maps and find similar patches through reflectance maps of successive frames to form the low-rank prior to make use of temporal correspondence. Our method performs well for a wide variety of images and videos, and achieves better quality both in enhancing and denoising, compared with the state-of-the-art methods.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3544e1be7260a3f8e78249177691180ae434db14

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In Computer Vision and Pattern Recognition

Citations in the corpus

URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement (2022, methodology): “For LLIE, Gamma correction is widely used to brighten up the illumination map [9, 22, 30], i.”
An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, methodology): “Many enhancement algorithms have been proposed based on Retinex theory [116]–[119].”
Low-Light Image and Video Enhancement Using Deep Learning: A Survey (2021, methodology): “Traditional methods for low-light enhancement include Histogram Equalization-based methods [35], [36] and Retinex model-based methods [37], [38], [39], [40], [41], [42], [43], [44].”
Underwater Image Enhancement With Hyper-Laplacian Reflectance Priors (2022, positive): “Under the framework [14], Ren et al. [18] introduced a low-rank prior into the retinex decomposition to suppress noise in the reﬂectance.”