A Lightweight Feature Distillation and Enhancement Network for Super-Resolution Remote Sensing Images

Abstract

Super-resolution (SR) images based on deep networks have achieved great accomplishments in recent years, but the large number of parameters that come with them are not conducive to use in equipment with limited capabilities in real life. Therefore, we propose a lightweight feature distillation and enhancement network (FDENet). Specifically, we propose a feature distillation and enhancement block (FDEB), which contains two parts: a feature-distillation part and a feature-enhancement part. Firstly, the feature-distillation part uses the stepwise distillation operation to extract the layered feature, and here we use the proposed stepwise fusion mechanism (SFM) to fuse the retained features after stepwise distillation to promote information flow and use the shallow pixel attention block (SRAB) to extract information. Secondly, we use the feature-enhancement part to enhance the extracted features. The feature-enhancement part is composed of well-designed bilateral bands. The upper sideband is used to enhance the features, and the lower sideband is used to extract the complex background information of remote sensing images. Finally, we fuse the features of the upper and lower sidebands to enhance the expression ability of the features. A large number of experiments show that the proposed FDENet both produces less parameters and performs better than most existing advanced models.

Lightweight interactive feature inference network for single-image super-resolution

“…Both RS-1 and RS-2 contain 120 images and cover diverse scenes with complicated image patterns. We exploit existing remote-sensing SR methods for comparison, including SRCNN 1 , VDSR 15 , LGCNet 65 , LapSRN 39 , IDN 19 , LESRCNN 44 , CARN-M 6 , FENet 63 , FDENet 66 , and DRAN 67 . All the aforementioned methods are directly evaluated on remote sensing data utilizing pre-trained models provided by relevant workers.…”