标题： 渐进式对齐退化学习用于全色锐化 显示英文标题

标题： Progressive Alignment Degradation Learning for Pansharpening

摘要： 基于深度学习的全色锐化已被证明能有效生成高分辨率多光谱（HRMS）图像。 为了创建监督的地面真实HRMS图像，通常使用Wald协议生成的合成数据。 该协议假设在人工低分辨率数据上训练的网络在高分辨率数据上也能表现良好。 然而，训练良好的模型通常在降分辨率和全分辨率数据集之间表现出性能权衡。 在本文中，我们深入研究了Wald协议，并发现其对真实世界退化模式的不准确近似限制了深度全色锐化模型的泛化能力。 为了解决这个问题，我们提出了渐进对齐退化模块（PADM），该模块通过两个子网络PAlignNet和PDegradeNet之间的相互迭代，自适应地学习准确的退化过程，而无需依赖预定义的算子。 在此基础上，我们引入了HFreqdiff，该方法将高频细节嵌入到扩散框架中，并结合CFB和BACM模块进行频率选择性细节提取和精确反向过程学习。 这些创新使得高分辨率全色和多光谱图像的有效整合成为可能，显著提高了空间锐度和质量。 实验和消融研究证明了所提出方法在性能上优于最先进的技术。 显示英文摘要

摘要： 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.