Zero-Reference Deep Curve Estimation for 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.

1. Introduction

Many photos are often captured under suboptimal lighting conditions due to inevitable environmental and/or technical constraints. These include inadequate and unbalanced lighting conditions in the environment, incorrect placement of objects against extreme back light, and under-exposure during image capturing. Such low-light photos suffer from compromised aesthetic quality and unsatisfactory transmission of information. The former affects viewers' experience while the latter leads to wrong message being communicated, such as inaccurate object/face recognition. Figure 1:

Visual comparisons on a typical low-light image. The proposed Zero-DCE achieves visually pleasing result in terms of brightness, color, contrast, and naturalness, while existing methods either fail to cope with the extreme back light or generate color artifacts. In contrast to other deep learning-based methods, our approach is trained without any reference image.