Structure-based low-rank Retinex model for low-light image enhancement

Liqian Wang; Qi Ge; Wenze Shao; Pengfei Wu; Haisong Deng

doi:10.1117/12.2589409

27 January 2021 Structure-based low-rank Retinex model for low-light image enhancement

Liqian Wang, Qi Ge, Wenze Shao, Pengfei Wu, Haisong Deng

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172016 (2021) https://doi.org/10.1117/12.2589409
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

In this paper, we propose a structure-based low-rank Retinex model for simultaneous low-light image enhancement and noise removal. Based on the traditional variational-based Retinex framework, in the proposed model, a smooth prior is forced on the illumination, and a gradient fidelity term and the weighted nuclear norm are used to suppress noise and enhance structural details in the reflectance. By considering that the manifold structure similarity is more effective than intensity similarity in describing the structural features of image patches, we further propose to use the manifold structure similarity in image patch grouping. Then, an alternating direction minimization algorithm is used to solve the reflectance estimating model. The entire process for solving the proposed model uses a sequential optimization. The final enhancement results is obtained by combining the reflectance and the Gamma corrected illumination. Experiment show that, the proposed method can simultaneously enhance and denoise the low-light image, and produce better or comparable results compared with the state-of-the-art methods

Citation Download Citation

Liqian Wang, Qi Ge, Wenze Shao, Pengfei Wu, and Haisong Deng "Structure-based low-rank Retinex model for low-light image enhancement", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172016 (27 January 2021); https://doi.org/10.1117/12.2589409

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