Local-global coupling relationship based low-light image enhancement

Abstract

Convolutional neural networks have made significant progress in various fields of artificial intelligence. However, the convolution operation which is based on a shared parameter sliding window mechanism only focuses on modeling the local relationship and is insufficient to establish global relationships. Nonetheless, the local and global relationships are both crucial for feature representation. Therefore, this paper concentrates on how to efficiently construct and couple the local and global relationships to mine more abundant feature information and enhance the discriminability of features. To this end, a local-global coupling module is designed, which is composed of four basic components: feature extraction, local relationship branch based on depth-wise convolution (DWConv), global relationship branch based on multi-head self-attention (MHSA), and local-global relationship coupling based on point-wise convolution (PWConv). On this basis, a local-global relationship coupling with encoder-decoder structure neural network is proposed, which can efficaciously model the local-global coupling relationship, strengthen the discriminative ability of feature information, and improve the performance of the model. Extensive experiments on low-light image enhancement datasets demonstrate the effectiveness of the proposed method, which significantly outperforms the state-of-the-art counterparts. Furthermore, the importance and effectiveness of local-global relationship coupling are analyzed through ablation and extended experiments.