Lightweight Multi-Attention Fusion Network for Image Super-Resolution

Abstract

Single image super-resolution reconstruction (SISR) is one of the important techniques in computer vision and image processing. Most of the existing SISR methods adopt equal processing for different spatial domains and channel domains, resulting in a large amount of computational resources wasted on unimportant features. In order to address these problems, a novel lightweight multi-attention fusion network (LMAFN) is proposed, in which the multiple attention fusion block allocates computational resources more efficiently by capturing the weight information implied by the channel domain and the spatial domain separately, thus effectively reducing the number of parameters. The synthetic channel attention block in the multiple attention fusion block makes full use of inter-channel correlation by introducing both global standard deviation pooling and maximum pooling. Global features are fused through residual linking to alleviate the problem of high frequency information loss. Experimental results on several benchmark datasets show that the proposed method effectively reduces the number of parameters and computational effort without excessive loss of reconstruction performance, and achieves better performance than the compared models.