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.

翻译：评估低光照图像增强（LLE）的性能具有高度主观性，因此将人类偏好融入图像增强成为必要。现有方法未能考虑这一点，而是提出了一系列可能有效的启发式标准来训练增强模型。本文提出一种新范式，即美学引导的低光照图像增强（ALL-E），将美学偏好引入LLE，并利用美学奖励在强化学习框架中激励训练。每个像素作为智能体，通过递归动作（即依次估计其对应的调整曲线）进行自我优化。大量实验表明，整合美学评估既改善了主观体验，也提升了客观评价。我们在多个基准上的结果证明了ALL-E相对于现有最先进方法的优越性。