Unsupervised Underexposed Image Enhancement via Self-Illuminated and Perceptual Guidance
Naishan Zheng, Jie Huang, Feng Zhao, Xueyang Fu, Feng Wu
Abstract
Underexposed images inevitably suffer severe degradation due to light distortion and noise corruption. Motivated by the limited samples of paired datasets, several unsupervised enhancement methods have been developed. However, these techniques heavily rely on pre-defined fixed lightness and noise removal constraints. Correspondingly, they cannot match the image-specific lightness when performing enhancement and can only refine details in a non-perceptual way. In this paper, we propose an Unsupervised Underexposed Image Enhancement Network (U2IENet) with self-illuminated and perceptual guidance. Specifically, to adjust the illumination for matching the image-specific lightness adaptively, we utilize the bright area of the underexposed image as the self-illuminated guidance to constrain the training process and modulate the features. Meanwhile, we introduce the perceptual guidance as a constraint to remove the noise based on illumination distribution, thus refining the details perceptually. Experiments on both underexposed datasets and public low-light datasets demonstrate the superiority of the proposed approach with higher flexibility over state-of- the-art solutions. In addition, our U2IENet also provides a side function that enables users to adjust the lightness via interactive tuning of a single parameter.