Aesthetics-Guided Low-Light Enhancement

Abstract

Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into LLE a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training LLE models. In this paper, we propose a new paradigm, i.e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward. Each pixel, functioning as an agent, refines itself by recursive actions. We further present ALL-E+, an extended version of ALL-E, which casts a two-stage aesthetics-guided enhancement and denoising. ALL-E+ achieves low-light enhancement and denoising compensation sequentially in a unified framework, resulting in significant improvements in both subjective visual experience and objective evaluation. Extensive experiments show that integrating aesthetic preferences can further improve the visual experience of enhanced images. Our results on various benchmarks also demonstrate the superiority of our method over state-of-the-art methods.