Transformer-Based and Structure-Aware Dual-Stream Network for Low-Light Image Enhancement

In this paper, we propose an end-to-end Transformer-based and structure-aware dual-stream network for low-light image enhancement. First, we divide the dual-stream network into a main stream and a structure stream. The main stream is used to recover an enhanced image and supply structural information to the structure stream, whereas the structure stream is designed to extract structural features from the main stream to provide rich structural information for the enhancement process. Second, we devise a structure-gated Transformer to balance the extraction of global and local features through parallel multihead self-attention with convolution operations following a multilayer perceptron, thus extracting sufficient structural information from the main stream in the encoder part of the dual-stream network. Finally, we develop a cross-attention-based feature fusion module that divides different window sizes into distinct feature fusion stages to achieve multistage, multiscale and multitype feature fusion in the decoder part of the dual-stream network. The experimental results show that our method not only works effectively but also offers favourable computational and storage overheads.