ALL-E: Aesthetics-guided Low-light Image Enhancement
This addresses the subjective nature of image enhancement for users by introducing a new paradigm that incorporates aesthetics, though it is incremental in applying reinforcement learning to this domain.
The paper tackles the problem of low-light image enhancement by integrating human aesthetic preferences into the training process, resulting in improved subjective and objective performance on benchmarks compared to state-of-the-art methods.
Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training enhancement 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, i.e., its corresponding adjustment curve is estimated sequentially. Extensive experiments show that integrating aesthetic assessment improves both subjective experience and objective evaluation. Our results on various benchmarks demonstrate the superiority of ALL-E over state-of-the-art methods.