Li Chongyi, Guo Chunle, Loy Chen Change
IEEE Trans Pattern Anal Mach Intell. 2022 Aug;44(8):4225-4238. doi: 10.1109/TPAMI.2021.3063604. Epub 2022 Jul 1.
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 is made publicly available at https://li-chongyi.github.io/Proj_Zero-DCE++.html.
本文提出了一种新颖的方法——零参考深度曲线估计（Zero-DCE），该方法将光照增强表述为使用深度网络进行特定图像曲线估计的任务。我们的方法训练了一个轻量级深度网络DCE-Net，以估计用于给定图像动态范围调整的逐像素和高阶曲线。曲线估计经过专门设计，考虑了像素值范围、单调性和可微性。Zero-DCE在对参考图像的宽松假设方面很有吸引力，即它在训练期间不需要任何配对甚至未配对的数据。这是通过一组精心制定的非参考损失函数实现的，这些函数隐式地衡量增强质量并驱动网络学习。尽管方法简单，但我们表明它能很好地推广到各种光照条件。我们的方法效率高，因为图像增强可以通过直观简单的非线性曲线映射来实现。我们进一步提出了Zero-DCE的加速轻量化版本，称为Zero-DCE++，它利用了一个仅有10K参数的微小网络。Zero-DCE++具有快速推理速度（对于大小为1200×900×3的图像，在单个GPU/CPU上分别为1000/11 FPS），同时保持Zero-DCE的增强性能。在各种基准上进行的大量实验从定性和定量方面证明了我们的方法优于现有方法。此外，还讨论了我们的方法在黑暗中进行人脸检测的潜在好处。源代码可在https://li-chongyi.github.io/Proj_Zero-DCE++.html上公开获取。
