用扩散模型解决遥感图像超分难题，支持极高倍率放大。
WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images
- 将超分重定义为解条件扩散方程，分步重构小波域高低频成分。
- 在16倍放大下仍保持图像全局一致与局部清晰，优于传统方法。
- 适合需要极端放大且追求高保真度的遥感图像处理任务。
深度神经网络在遥感超分辨率（SR）领域取得显著进展，但多数方法受限于低放大倍率（如2或4倍），因高倍率下问题愈发病态。为此，我们将高倍率超分重新定义为超分辨率（UR）问题，将其重构为求解条件扩散随机微分方程（SDE）。在此框架下，提出WaveDiffUR——一种基于小波域的扩散超分辨率求解器，将超分辨率过程分解为逐级子过程，分别处理条件小波成分。该方法通过集成预训练的SR模型作为即插即用模块，迭代重建低频细节（保证全局一致性）与高频成分（提升局部保真度），从而缓解SDE的病态性并实现跨应用可扩展性。针对极端放大下的固定边界限制，引入跨尺度金字塔（CSP）约束，一种动态自适应机制，引导生成精细小波细节，在极高放大倍率下仍能输出一致且高保真的结果。
原文摘要 · Abstract (English)
Deep neural networks have recently achieved significant advancements in remote sensing superresolu-tion (SR). However, most existing methods are limited to low magnification rates (e.g., 2 or 4) due to the escalating ill-posedness at higher magnification scales. To tackle this challenge, we redefine high-magnification SR as the ultra-resolution (UR) problem, reframing it as solving a conditional diffusion stochastic differential equation (SDE). In this context, we propose WaveDiffUR, a novel wavelet-domain diffusion UR solver that decomposes the UR process into sequential sub-processes addressing conditional wavelet components. WaveDiffUR iteratively reconstructs low-frequency wavelet details (ensuring global consistency) and high-frequency components (enhancing local fidelity) by incorporating pre-trained SR models as plug-and-play modules. This modularity mitigates the ill-posedness of the SDE and ensures scalability across diverse applications. To address limitations in fixed boundary conditions at extreme magnifications, we introduce the cross-scale pyramid (CSP) constraint, a dynamic and adaptive framework that guides WaveDiffUR in generating fine-grained wavelet details, ensuring consistent and high-fidelity outputs even at extreme magnification rates.
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