  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, DCE-Net, 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 even unpaired data during training. This is achieved
through a set of carefully formulated non-reference loss functions, which
implicitly measure the enhancement quality and drive the learning of the
network. Despite its simplicity, we show that it generalizes well to diverse
lighting conditions. Our method is efficient as image enhancement can be
achieved by an intuitive and 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 a single GPU/CPU for an image of size
1200*900*3) while keeping the enhancement performance of Zero-DCE. Extensive
experiments on various benchmarks demonstrate the advantages of our method over
state-of-the-art methods qualitatively and quantitatively. Furthermore, the
potential benefits of our method to face detection in the dark are discussed.
The source code will be made publicly available at
https://li-chongyi.github.io/Proj_Zero-DCE++.html.
