URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement

Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement. However, the commonly used handcrafted priors and optimization-driven solutions lead to the absence of adaptivity and efficiency. To address these issues, in this paper, we propose a Retinex-based deep unfolding network (URetinex-Net), which unfolds an optimization problem into a learnable network to decompose a low-light image into reflectance and illumination layers. By formulating the decomposition problem as an implicit priors regularized model, three learning-based modules are carefully designed, responsible for data-dependent initialization, high-efficient unfolding optimization, and user-specified illumination enhancement, respectively. Particularly, the proposed unfolding optimization module, introducing two networks to adaptively fit implicit priors in data-driven manner, can realize noise suppression and details preservation for the final decomposition results. Extensive experiments on real-world low-light images qualitatively and quantitatively demonstrate the effectiveness and superiority of the proposed method over state-of-the-art methods. The code is available at https://github.com/AndersonYong/URetinex-Net.

Automated summary

In this paper, we propose a Retinex-based deep unfolding network (URetinex-Net) to enhance low-light images. By unfolding an optimization problem into a learnable network, the image can be decomposed into reflectance and illumination layers. By formulating the decomposition problem as an implicit priors regularized model, three learning-based modules are designed for data-dependent initialization, high-efficient optimization, and user-specified illumination enhancement. Experimental results demonstrate the effectiveness and superiority of the proposed method in low-light image enhancement.