MTRBNet: Multi-Branch Topology Residual Block-Based Network for Low-Light Enhancement

Abstract

The learning-based low-light image enhancement methods have remarkable performance due to the robust feature learning and mapping capabilities. This paper proposes a multi-branch topology residual block (MTRB)-based network (MTRBNet), which can alleviate training difficulties and more efficiently use the parameters between neurons. Compared with the previous residual block, the proposed MTRB increases the width of the network and simultaneously transmits information along with the depth and width directions, which can effectively select network nodes to promote the network learning capacity. Meanwhile, the feature information of neighbor nodes is transferred to each other, thereby maximizing the information flow of the convolution unit. The proposed information connection and feedback mechanism can improve the network’s ability to capture the global and local features. We analyze the pros and cons of two multi-feature fusion strategies (i.e., addition and concatenation) and three normalization methods on the quantitative results. In addition, we embed our MTRB into traditional Encoder-Decoder structure to improve the image enhancement results under different low-light imaging conditions. Experiments on the LOL image dataset have demonstrated that our MTRBNet achieves superior performance compared with several state-of-the-art methods.