IRNet: An Improved Zero-Shot Retinex Network for Low-Light Image Enhancement

Abstract

Inadequate illumination often causes severe image degradation, such as noise and artifacts. These types of images do not meet the requirements of advanced visual tasks, so low-light image enhancement is currently a flourishing and challenging research topic. To alleviate the problem of low brightness and low contrast, this paper proposes an improved zero-shot Retinex network, named IRNet, which is composed of two parts: a Decom-Net and an Enhance-Net. The Decom-Net is designed to decompose the raw input into two maps, i.e., illuminance and reflection. Afterwards, the subsequent Enhance-Net takes the decomposed illuminance component as its input, enhances the image brightness and features through gamma transformation and a convolutional network, and fuses the enhanced illumination and reflection maps together to obtain the final enhanced result. Due to the use of zero-shot learning, no previous training is required. IRNet depends on the internal optimization of each individual input image, and the network weights are updated by iteratively minimizing a series of designed loss functions, in which noise reduction loss and color constancy loss are introduced to reduce noise and relieve color distortion during the image enhancement process. Experiments conducted on public datasets and the presented practical applications demonstrate that our method outperforms other counterparts in terms of both visual perception and objective metrics.