Lightweight Remote Sensing Image Super-Resolution via Background-Based Multiscale Feature Enhancement Network

In the field of remote sensing image super-resolution (RSISR), most methods based on convolutional neural networks (CNNs) tend to focus on high-weight features in the convolutional kernels, thus overlooking low-weight background features. This bias may result in the neglect of some important information in the background. To address this challenge, we propose a background-based multiscale feature enhancement network (BMFENet), which can extract and supplement missing features from different scale backgrounds to improve the reconstruction of remote sensing images (RSIs). Specifically, we constructed a large kernel feature supplement block (LFSB). The LFSB uses large kernel attention mechanism and multiscale mechanism to expand the receptive field, aggregating global information. Meanwhile, it generates background feature weights to increase the attention to neglected information, thereby reducing the distortion of detailed features. Furthermore, to enhance the nonlinear expression capability of the model, we designed a lattice gated unit (LGU). The LGU removes redundant information through a gating mechanism, efficiently aggregates useful channel information through interchannel interactions and attention mechanisms, and introduces directional convolution to make the model more adaptable to super-resolution (SR) tasks in complex scenes. We validated our method on two remote sensing and four SR benchmark datasets, and the results show that our approach achieves a good balance between performance and complexity.