Low-light image enhancement (LLIE) is a pervasive yet challenging problem,
since: 1) low-light measurements may vary due to different imaging conditions
in practice; 2) images can be enlightened subjectively according to diverse
preferences by each individual. To tackle these two challenges, this paper
presents a novel deep reinforcement learning based method, dubbed ReLLIE, for
customized low-light enhancement. ReLLIE models LLIE as a markov decision
process, i.e., estimating the pixel-wise image-specific curves sequentially and
recurrently. Given the reward computed from a set of carefully crafted
non-reference loss functions, a lightweight network is proposed to estimate the
curves for enlightening of a low-light image input. As ReLLIE learns a policy
instead of one-one image translation, it can handle various low-light
measurements and provide customized enhanced outputs by flexibly applying the
policy different times. Furthermore, ReLLIE can enhance real-world images with
hybrid corruptions, e.g., noise, by using a plug-and-play denoiser easily.
Extensive experiments on various benchmarks demonstrate the advantages of
ReLLIE, comparing to the state-of-the-art methods.

本研究提出了一种基于深度强化学习的新型低光图像增强方法，称为 ReLLIE，将 LLIE 建模为马尔可夫决策过程，使用轻量级网络计算像素级图像特定曲线，利用非参照损失函数计算的奖励来增强输入的低光图像，ReLLIE 不是进行一一图像转换，而是学习策略，从而可以处理各种低光测量数据，提供定制增强输出。

ReLLIE：深度强化学习用于定制化低光图像增强

ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image  Enhancement

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。