Super-resolution reconstruction of images based on residual dual-path interactive fusion combined with attention

In recent years, deep learning has made significant progress in the field of single-image super-resolution (SISR) reconstruction, which has greatly improved reconstruction quality. However, most of the SISR networks focus too much on increasing the depth of the network in the process of feature extraction and neglect the connections between different levels of features as well as the full use of low-frequency feature information. To address this problem, this work proposes a network based on residual dual-path interactive fusion combined with attention (RDIFCA). Using the dual interactive fusion strategy, the network achieves the effective fusion and multiplexing of high- and low-frequency information while increasing the depth of the network, which significantly enhances the expressive ability of the network. The experimental results show that the proposed RDIFCA network exhibits certain superiority in terms of objective evaluation indexes and visual effects on the Set5, Set14, BSD100, Urban100, and Manga109 test sets.