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：本文提出了一种两阶段低光照图像增强方法，称为自参考深度自适应曲线估计（Self-DACE）。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。该算法基于一种新型低光照增强曲线，可用于局部提升图像亮度。我们还提出了一种新的损失函数，结合简化的物理模型，旨在保留自然图像的颜色、结构和保真度。我们使用普通卷积神经网络（vanilla CNN）通过深度自适应调整曲线（AAC）逐像素映射，同时保留局部图像结构。其次，我们引入了相应的去噪方案，以消除暗部潜在的噪声。我们对暗部噪声进行近似建模，并部署去噪网络（Denoising-Net）在第一阶段后估计并去除噪声。详尽的定性和定量分析表明，我们的方法在多个真实世界数据集上优于现有最先进算法。