RFANSR: Receptive Field Aggregation Network for Lightweight Remote Sensing Image Super-Resolution
Xiaoyu Yan, Wei Guo Song, Xiaotong Feng, Wei Guo, Keqing Ning · Remote Sensing · 2025
Expanding the receptive field while maintaining efficiency is a key challenge in lightweight remote sensing super-resolution. Existing methods often suffer from parameter redundancy or insufficient channel utilization. To address these issues, we propose the Receptive Field Aggregation Network (RFANSR). First, we design a Progressive Receptive Field Aggregator (PRFA). It expands the receptive field by cascading medium-sized kernels, avoiding the heavy overhead of extremely large kernels. Second, we introduce a Statistical Guidance Module (SGM). This module replaces inefficient identity mappings with statistical channel recalibration to maximize feature utility. Additionally, we propose a Spatial-Gated Feed-Forward Network (SGFN) to reduce information loss via spatial attention. Extensive experiments demonstrate that RFANSR outperforms state-of-the-art lightweight models. Notably, RFANSR achieves PSNR improvements of 0.06 dB on RSCNN7 and 0.14 dB on DOTA datasets. Remarkably, it requires only 383 K parameters, representing a 45.4% reduction compared to DLKN.