MB-SCRSR: A Multi-branch Image Super-Resolution Network Based on Spatial and Channel Reconstruction

In recent years, convolutional neural network has been widely used in single image super-resolution. As the depth of the network increases, it is difficult to effectively balance the convergence of training with the improvement of performance. Therefore, in this paper, we propose a multi-branch image super-resolution network based on spatial and channel reconstruction: MB-SCRSR. In MB-SCRSR, we construct a multi branch network to better extract nonlinear features in the feature space. Additionally, the network introduces a fusion attention unit to effectively fuse the extracted features across branches, thereby capturing more relevant information. Finally, MB-SCRSR incorporates spatial and channel reconstruction blocks to reduce redundancy in both spatial and channel dimensions. Experimental results demonstrate that MB-SCRSR outperforms state-of-the-art models in terms of objective evaluation metrics, with a peak signal-to-noise ratio improvement ranging from 0.1 to 0.6 dB.