Lightweight Dual Domain Attention Aggregation Network for Remote Sensing Image Super-Resolution

Abstract

Significant progress has been made in remote sensing image super-resolution based on deep neural networks. However, existing methods typically suffer from parameter redundancy and high computational costs, making them difficult to deploy on resource-constrained edge devices. Moreover, the image reconstruction process often involves phenomena such as texture blurring and edge distortion, which degrade the restoration quality. To address these issues, we propose a lightweight dual-domain attention aggregation network (LDANet), aiming to achieve image super-resolution with both high-efficiency and high-quality. Specifically, LDANet is composed of multiple cascaded dual domain attention-guided feature aggregation blocks (DAGBs). Within each DAGB, we propose the variance-aware spatial attention module, which enhances the extraction capability of nonlocal salient features through a dual-path mechanism that integrates parallel adaptive pooling and feature variance modulation strategies, thereby improving the discriminability of spatial features while effectively suppressing noise interference. To further optimize pixel-level detail expression, we propose the pixel-embedding channel attention module, which achieves cross-channel global context awareness by jointly modeling pixel-level spatial relationships and channel-wise self-attention, thereby enhancing texture fidelity and edge clarity. Moreover, asymmetric convolutional gated feed-forward network is introduced to enhance the feature aggregation and local context mixing capabilities within the DAGB. Comparative experiments demonstrate that LDANet achieves superior reconstruction performance on multiple datasets with smaller model sizes, exhibiting better potential for practical applications. The source code of LDANet will be released at https://github.com/AHUT-MILAGroup/LDANet.