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. Source code and models are in the project page.

翻译：低光照图像增强（LLE）性能的评估具有高度主观性，因此将人类偏好融入图像增强过程成为一种必要。现有方法未能考虑这一点，并提出了一系列可能有效的启发式标准用于训练增强模型。本文提出一种新范式，即美学引导的低光照图像增强（ALL-E），该范式将美学偏好引入LLE，并通过美学奖励在强化学习框架中激励模型训练。每个像素作为智能体，通过递归动作（即其对应的调整曲线被顺序估计）进行自我优化。大量实验表明，融入美学评估可同时提升主观体验与客观评价。我们在多个基准数据集上的结果证明了ALL-E相较于现有最先进方法的优越性。源代码与模型已发布于项目主页。