Single Image Super Resolution via Multi-Attention Fusion Recurrent Network

Deep convolutional neural networks have significantly enhanced the performance of single image super-resolution in recent years. However, the majority of the proposed networks are single-channel, making it challenging to fully exploit the advantages of neural networks in feature extraction. This paper proposes a Multi-attention Fusion Recurrent Network (MFRN), which is a multiplexing architecture-based network. Firstly, the algorithm reuses the feature extraction part to construct the recurrent network. This technology reduces the number of network parameters, accelerates training, and captures rich features simultaneously. Secondly, a multiplexing-based structure is employed to obtain deep information features, which alleviates the issue of feature loss during transmission. Thirdly, an attention fusion mechanism is incorporated into the neural network to fuse channel attention and pixel attention information. This fusion mechanism effectively enhances the expressive power of each layer of the neural network. Compared with other algorithms, our MFRN not only exhibits superior visual performance but also achieves favorable results in objective evaluations. It generates images with sharper structure and texture details and achieves higher scores in quantitative tests such as image quality assessment.