  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.
