Two-Stage Random Alternation Framework for Zero-Shot Pansharpening

In recent years, pansharpening has seen rapid advancements with deep learning methods, which have demonstrated impressive fusion quality. However, the challenge of acquiring real high-resolution images limits the practical applicability of these methods. To address this, we propose a two-stage random alternating framework (TRA-PAN) that effectively integrates strong supervision constraints from reduced-resolution images with the physical characteristics of full-resolution images. The first stage introduces a pre-training procedure, which includes Degradation-Aware Modeling (DAM) to capture spatial-spectral degradation mappings, alongside a warm-up procedure designed to reduce training time and mitigate the negative effects of reduced-resolution data. In the second stage, Random Alternation Optimization (RAO) is employed, where random alternating training leverages the strengths of both reduced- and full-resolution images, further optimizing the fusion model. By primarily relying on full-resolution images, our method enables zero-shot training with just a single image pair, obviating the need for large datasets. Experimental results demonstrate that TRA-PAN outperforms state-of-the-art (SOTA) methods in both quantitative metrics and visual quality in real-world scenarios, highlighting its strong practical applicability.