In this paper, we propose a 2-stage low-light image enhancement method called Self-Reference Deep Adaptive Curve Estimation (Self-DACE). In the first stage, we present an intuitive, lightweight, fast, and unsupervised luminance enhancement algorithm. The algorithm is based on a novel low-light enhancement curve that can be used to locally boost image brightness. We also propose a new loss function with a simplified physical model designed to preserve natural images' color, structure, and fidelity. We use a vanilla CNN to map each pixel through deep Adaptive Adjustment Curves (AAC) while preserving the local image structure. Secondly, we introduce the corresponding denoising scheme to remove the latent noise in the darkness. We approximately model the noise in the dark and deploy a Denoising-Net to estimate and remove the noise after the first stage. Exhaustive qualitative and quantitative analysis shows that our method outperforms existing state-of-the-art algorithms on multiple real-world datasets.

翻译：本文提出一种名为自参考深度自适应曲线估计（Self-DACE）的两阶段低光照图像增强方法。第一阶段，我们提出了一种直观、轻量、快速且无监督的亮度增强算法。该算法基于一种新颖的低光照增强曲线，可局部提升图像亮度。同时，我们设计了一种带有简化物理模型的新型损失函数，用于保持自然图像的颜色、结构与保真度。通过使用标准卷积神经网络（vanilla CNN），在保持局部图像结构的同时，为每个像素映射深度自适应调整曲线（AAC）。第二阶段，我们引入了相应的去噪方案以消除暗部潜在噪声。我们近似建模暗部噪声，并部署去噪网络（Denoising-Net）在第一阶段后进行噪声估计与去除。详尽的定性与定量分析表明，该方法在多个真实世界数据集上优于现有最先进算法。