Interpretable Optimization-Inspired Unfolding Network for Low-Light Image Enhancement

Abstract

Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement (LLIE). However, the hand-crafted priors and conventional optimization algorithm adopted to solve the layer decomposition problem result in the lack of adaptivity and efficiency. To this end, this paper proposes 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 fairly-flexible component adjustment, respectively. Particularly, the proposed unfolding optimization module, introducing two networks to adaptively fit implicit priors in the data-driven manner, can realize noise suppression and details preservation for decomposed components. URetinex-Net++ is a further augmented version of URetinex-Net, which introduces a cross-stage fusion block to alleviate the color defect in URetinex-Net. Therefore, boosted performance on LLIE can be obtained in both visual quality and quantitative metrics, where only a few parameters are introduced and little time is cost. Extensive experiments on real-world low-light images qualitatively and quantitatively demonstrate the effectiveness and superiority of the proposed URetinex-Net++ over state-of-the-art methods.