Attention-Guided Progressive Frequency-Decoupled Network for Pan-Sharpening

Existing pan-sharpening methods focus more on the processing of spatial domain features, which is difficult to balance for multiresolution performance and prone to difficulties such as edge blurring or artifacts. In this article, we propose an attention-guided progressive frequency-decoupled network, termed APFNN, to improve the performance of fused images in terms of spatial enhancement and spectral fidelity. The decoupling and fusion of the frequency-domain features are used as the main body, and the two-stage progressive network framework is built with the auxiliary correction by the spatial domain features, realizing fine-grained interactive fusion of dual-domain features. To optimize the APFNN, spatial feature (SF) characteristics processed with cascading interactive attention (CIAtt) are used as guidance information, combined with the features of the high-resolution panchromatic (HRPAN) image and low-resolution multispectral (LRMS) image for high-frequency and low-frequency decoupling, embedded into the residual dense module to form a new feature extraction component and perform subtle frequency feature fusion. Extensive qualitative and quantitative experiments have been conducted on a variety of datasets, verifying that the proposed APFNN outperforms state-of-the-art methods both in reduced-resolution and full-resolution image pan-sharpening.