Adaptive Low-Light Image Enhancement Optimization Framework with Algorithm Unrolling

Abstract

Images captured in a dark environment may cause low visibility and lose significant details leading to poor performance of vision-based recognition systems. Recently, deep learning-based methods have been proposed for low-light image enhancement (LIE) with different priors or training schemes. However, even those LIE methods may introduce visual artifacts into the enhanced images. This paper proposes an adaptive LIE optimization framework that allows to re-optimize different deep learning-based LIE methods based on an adaptive quality evaluation (QE). Specifically, we design an interpretable and learnable LIE-QE module for LIE. To find the optimal structure of the LIE-QE module, we propose an algorithm unrolling method to design the LIE-QE module, where the each layer of the decomposition component of the LIE-QE module can be interpreted as WLS edge-aware smoothing. Both qualitative and quantitative experiments were conducted, and the evaluation verified the effectiveness of the proposed learnable deep unrolled LIE-QE module for LIE. The results shows that the proposed LIE framework can effectively improve different deep learning-based LIE methods indicating the potential of the optimization framework with LIE-QE module to re-optimize existing DNN-based LIE methods.