Progressive Alignment Degradation Learning for Pansharpening
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
Deep learning-based pansharpening has been shown to effectively generate
high-resolution multispectral (HRMS) images. To create supervised ground-truth
HRMS images, synthetic data generated using the Wald protocol is commonly
employed. This protocol assumes that networks trained on artificial
low-resolution data will perform equally well on high-resolution data. However,
well-trained models typically exhibit a trade-off in performance between
reduced-resolution and full-resolution datasets. In this paper, we delve into
the Wald protocol and find that its inaccurate approximation of real-world
degradation patterns limits the generalization of deep pansharpening models. To
address this issue, we propose the Progressive Alignment Degradation Module
(PADM), which uses mutual iteration between two sub-networks, PAlignNet and
PDegradeNet, to adaptively learn accurate degradation processes without relying
on predefined operators. Building on this, we introduce HFreqdiff, which embeds
high-frequency details into a diffusion framework and incorporates CFB and BACM
modules for frequency-selective detail extraction and precise reverse process
learning. These innovations enable effective integration of high-resolution
panchromatic and multispectral images, significantly enhancing spatial
sharpness and quality. Experiments and ablation studies demonstrate the
proposed method's superior performance compared to state-of-the-art techniques.
