Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation.

This paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. Our method trains a lightweight deep network to estimate pixel-wise and high-order curves for dynamic range adjustment of a given image. The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or unpaired data during training. This is achieved through a set of carefully formulated non-reference losses, which implicitly measure the enhancement quality and drive the learning of the network. Despite its simplicity, it generalizes well to diverse lighting conditions. Our method is efficient as image enhancement can be achieved by a simple nonlinear curve mapping. We further present an accelerated and light version of Zero-DCE, called Zero-DCE++, that takes advantage of a tiny network with just 10K parameters. Zero-DCE++ has a fast inference speed (1000/11 FPS on single GPU/CPU) while keeping the enhancement performance of Zero-DCE. Experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods. The potential benefits of our method to face detection in the dark are discussed.