HCTMIF: Hybrid CNN-Transformer Multi Information Fusion Network for Low Light Image Enhancement

Abstract

Images captured with poor hardware and insufficient light sources suffer from visual degradation such as low visibility, strong noise, and color casts. Low-light image enhancement methods focus on solving the problem of brightness in dark areas while eliminating the degradation of low-light images. To solve the above problems, we proposed a hybrid CNN-transformer multi information fusion network (HCTMIF) for low-light image enhancement. In this paper, the proposed network architecture is divided into three stages to progressively improve the degraded features of low-light images using the divide-and-conquer principle. First, both the first stage and the second stage adopt the encoder–decoder architecture composed of transformer and CNN to improve the long-distance modeling and local feature extraction capabilities of the network. We add a visual enhancement module (VEM) to the encoding block to further strengthen the network's ability to learn global and local information. In addition, the multi-information fusion block (MIFB) is used to complement the feature maps corresponding to the same scale of the coding block and decoding block of each layer. Second, to improve the mobility of useful information across stages, we designed the self-supervised module (SSM) to readjust the weight parameters to enhance the characterization of local features. Finally, to retain the spatial details of the enhanced images more precisely, we design the detail supplement unit (DSU) to enrich the saturation of the enhanced images. After qualitative and quantitative analyses on multiple benchmark datasets, our method outperforms other methods in terms of visual effects and metric scores.