Lightweight Arbitrary-Scale Super-Resolution of Remote Sensing Images via Super-Scale Feature

Yifei Long, Yuantong Zhang, Daiqin Yang, Zhenzhong Chen, Huairui Wang, Shan Liu

Abstract: Remote sensing image (RSI) super-resolution (SR) demands lightweight and efficient methods due to required rapid response in practical applications. Integrating RSIs with different resolutions for diverse applications also requires arbitrary-scale SR, making fix-scaled SR scale inflexible. Therefore, a lightweight SR algorithm capable of arbitrary-scale is necessary for RSIs. To address the above issue, a super-scale feature-based lightweight arbitrary-scale (SFLA) SR network is proposed in this paper. The network consists of two modules: 1) A super-scale feature extraction (SSFE) module that extracts features at both the initial low-resolution (LR) and an integer super-scale resolution, 2) A self-attention implicit function reconstruction (SIFR) module that utilizes multi-layer perceptron (MLP) network and self-attention mechanism for pixel-wise feature mapping to achieve superior SR results. Comparative experiments and ablation results demonstrate that the proposed SFLA algorithm effectively strikes a good balance between performance and complexity.