  The 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 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. Our
method is efficient as image enhancement can be achieved by an intuitive and
simple nonlinear curve mapping. Despite its simplicity, we show that it
generalizes well to diverse lighting conditions. 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 Zero-DCE to face detection in the dark are discussed.
Code and model will be available at https://github.com/Li-Chongyi/Zero-DCE.
