  Optical satellite images are a critical data source; however, cloud cover
often compromises their quality, hindering image applications and analysis.
Consequently, effectively removing clouds from optical satellite images has
emerged as a prominent research direction. While recent advancements in cloud
removal primarily rely on generative adversarial networks, which may yield
suboptimal image quality, diffusion models have demonstrated remarkable success
in diverse image-generation tasks, showcasing their potential in addressing
this challenge. This paper presents a novel framework called DiffCR, which
leverages conditional guided diffusion with deep convolutional networks for
high-performance cloud removal for optical satellite imagery. Specifically, we
introduce a decoupled encoder for conditional image feature extraction,
providing a robust color representation to ensure the close similarity of
appearance information between the conditional input and the synthesized
output. Moreover, we propose a novel and efficient time and condition fusion
block within the cloud removal model to accurately simulate the correspondence
between the appearance in the conditional image and the target image at a low
computational cost. Extensive experimental evaluations on two commonly used
benchmark datasets demonstrate that DiffCR consistently achieves
state-of-the-art performance on all metrics, with parameter and computational
complexities amounting to only 5.1% and 5.4%, respectively, of those previous
best methods. The source code, pre-trained models, and all the experimental
results will be publicly available at https://github.com/XavierJiezou/DiffCR
upon the paper's acceptance of this work.
