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
this https URL

该论文提出了一种新颖的方法，称为 Zero-Reference Deep Curve Estimation (Zero-DCE)，它将图像增强的问题转化为深度网络中的图像特定曲线估计问题。该方法通过训练一个轻量级的深度网络，DCE-Net，针对给定图像估计像素级和高阶曲线以进行动态范围调整。Zero-DCE++ 是 Zero-DCE 的加速和轻量级版本，具有快速的推理速度，同时保持 Zero-DCE 的增强性能。

通过零参考深曲线估计学习提高低光图像

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

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 this https URL

该论文提出了一种新的零参考深曲线估计方法（Zero-DCE），它将受光增强任务视为深度网络中的图像特定曲线估计任务。我们通过训练一个轻量级的深度网络 DCE-Net 来估计给定图像的像素级和高阶曲线，以进行动态范围调整。Zero-DCE 有很强的适用性，即在训练期间不需要任何成对或无序数据进行参考。通过一组精心设计的非参考损失函数，我们实现了对非参考图像增强质量的隐式度量，并驱动网络学习。我们的方法具有高效性和可解释性，因为图像增强可以通过直观和简单的非线性曲线映射来实现。尽管简单，我们展示了它对不同光照条件的良好推广能力。在各种基准测试上进行的大量实验定量和定性地展示了我们方法的优势。此外，我们还讨论了 Zero-DCE 对于在黑暗中进行人脸检测的潜在优势。