HSFAN: A Dual-Branch Hybrid-Scale Feature Aggregation Network for Remote Sensing Image Super-Resolution

Abstract

To address the issues of insufficient feature utilization in high-entropy regions (such as complex textures and edges), difficulty in detail recovery, and excessive model parameters with high computational complexity in existing remote sensing image super-resolution networks, a novel dual-branch hybrid-scale feature aggregation network (HSFAN) is proposed. The design of this network aims to achieve an optimal balance between model complexity and reconstruction quality. The main branch of the HSFAN effectively expands the receptive field through a multi-scale parallel large convolution kernel (MSPLCK) module, enhancing the ability to model global structures that contain rich information, while maintaining consistency constraints in the feature space. Meanwhile, an enhanced parallel attention (EPA) module is incorporated, optimizing feature allocation by prioritizing high-entropy feature channels and spatial locations, thereby improving the expression of key details. The auxiliary branch is designed with a multi-scale large-kernel attention (MSLA) module, employing depthwise separable convolutions to significantly reduce the computational overhead in the feature processing path, while adaptive attention weighting strengthens the capture and reconstruction of local high-frequency information. Experimental results show that, for the ×4 super-resolution task on the UC Merced dataset, the proposed algorithm achieves a PSNR of 27.91 dB and an SSIM of 0.7616, outperforming most current mainstream super-resolution algorithms, while maintaining a low computational cost and model parameter count. This provides a new research approach and technical route for remote sensing image super-resolution reconstruction.