Long-Range Multi-Scale Fusion for Efficient Single Image Super-Resolution

Abstract

Improving the performance of single image super-resolution (SISR) via extending the effective receptive field (ERF) of the model has become an admired paradigm in the field due to the universal self-similarity prior of natural images. However, it cannot fully explore model capability by solely increasing the ERF to capture long-range dependencies as the non-local self-similarity is typically multi-scale and cross-scale. To this end, a Long-range Multi-scale Fusion Network (LMFN) is devised in this work to simultaneously excavate both long-range and multi-scale priors in images, and the interaction between the both. Within the same scale, our model employs large kernel attention (LKA) and multi-scale modulation (MSM) to learn long-range and multi-scale features. To exploit the interaction between long-range and multi-scale dependencies within one single scale and across scales, we design an Interactive Fusion Modulation (IFM) module for the effective fusion of the non-local and multi-scale features. Extensive experiments on the benchmark datasets illustrate the significant superiority of the proposed LMFN over the advanced SISR models.