提出新型去云模型EMRDM，直接建模有云到无云图像的演化过程。
Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space
- 基于均值回归扩散框架，设计可更新模块与清晰的设计空间。
- 在单时相和多时相数据集上均超越现有方法，显著提升去云效果。
- 适合遥感图像处理、环境监测等需要高质量无云影像的场景。
云去除（CR）在遥感图像处理中仍具挑战性。尽管扩散模型（DM）具备强大的生成能力，但其直接应用于CR时表现不佳，因其从随机噪声生成无云图像，忽视了有云输入中的固有信息。为此，我们提出基于均值回归扩散模型（MRDM）的新型去云模型EMRDM，建立有云与无云图像间的直接扩散过程。相比现有MRDM，EMRDM采用模块化框架，支持可更新模块与明确的设计空间，基于重构的前向过程和新的基于常微分方程（ODE）的反向过程。在此框架下，我们重新设计关键模块以提升性能：通过预条件技术重构去噪器，优化训练流程，并引入确定性与随机采样器改进采样过程。为实现多时相去云，进一步构建可同时处理序列图像的去噪网络。在单时相与多时相数据集上的实验表明，EMRDM性能显著优于现有方法。代码已开源：https://github.com/Ly403/EMRDM。
原文摘要 · Abstract (English)
Cloud removal (CR) remains a challenging task in remote sensing image processing. Although diffusion models (DM) exhibit strong generative capabilities, their direct applications to CR are suboptimal, as they generate cloudless images from random noise, ignoring inherent information in cloudy inputs. To overcome this drawback, we develop a new CR model EMRDM based on mean-reverting diffusion models (MRDMs) to establish a direct diffusion process between cloudy and cloudless images. Compared to current MRDMs, EMRDM offers a modular framework with updatable modules and an elucidated design space, based on a reformulated forward process and a new ordinary differential equation (ODE)-based backward process. Leveraging our framework, we redesign key MRDM modules to boost CR performance, including restructuring the denoiser via a preconditioning technique, reorganizing the training process, and improving the sampling process by introducing deterministic and stochastic samplers. To achieve multi-temporal CR, we further develop a denoising network for simultaneously denoising sequential images. Experiments on mono-temporal and multi-temporal datasets demonstrate the superior performance of EMRDM. Our code is available at https://github.com/Ly403/EMRDM.
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