  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.
