The presence of cloud layers severely compromises the quality and
effectiveness of optical remote sensing (RS) images. However, existing
deep-learning (DL)-based Cloud Removal (CR) techniques encounter difficulties
in accurately reconstructing the original visual authenticity and detailed
semantic content of the images. To tackle this challenge, this work proposes to
encompass enhancements at the data and methodology fronts. On the data side, an
ultra-resolution benchmark named CUHK Cloud Removal (CUHK-CR) of 0.5m spatial
resolution is established. This benchmark incorporates rich detailed textures
and diverse cloud coverage, serving as a robust foundation for designing and
assessing CR models. From the methodology perspective, a novel diffusion-based
framework for CR called Diffusion Enhancement (DE) is proposed to perform
progressive texture detail recovery, which mitigates the training difficulty
with improved inference accuracy. Additionally, a Weight Allocation (WA)
network is developed to dynamically adjust the weights for feature fusion,
thereby further improving performance, particularly in the context of
ultra-resolution image generation. Furthermore, a coarse-to-fine training
strategy is applied to effectively expedite training convergence while reducing
the computational complexity required to handle ultra-resolution images.
Extensive experiments on the newly established CUHK-CR and existing datasets
such as RICE confirm that the proposed DE framework outperforms existing
DL-based methods in terms of both perceptual quality and signal fidelity.

该研究提出了一种基于扩散增强的云去除框架，利用超分辨率图像生成和逐层训练策略，实现了对光学遥感图像中云层的准确去除，大幅提高了图像的感知质量和信号保真度。