Zero-CSC: Low-light image enhancement with zero-reference color self-calibration

Abstract

Zero-Reference Low-Light Image Enhancement (LLIE) techniques mainly focus on grey-scale inhomogeneities, and few methods consider how to explicitly recover a dark scene to achieve enhancements in color and overall illumination. In this paper, we introduce a novel Zero-Reference Color Self-Calibration framework for enhancing low-light images, termed as Zero-CSC. It effectively emphasizes channel-wise representations that contain fine-grained color information, achieving a natural result in a progressive manner. Furthermore, we propose a Light Up (LU) module with large-kernel convolutional blocks to improve overall illumination, which is implemented with a simple U-Net and further simplified with a light-weight structure. Experiments on representative datasets show that our model consistently achieves state-of-the-art performance in image signal-to-noise ratio, structural similarity, and color accuracy, setting new records on the challenging SICE dataset with improvements of 23.7% in image signal-to-noise ratio and 5.3% in structural similarity compared to the most advanced methods.