IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing Images
IEEE Transactions on Geoscience and Remote Sensing(2024)
摘要
Deep learning technologies have demonstrated their effectiveness in removing
cloud cover from optical remote-sensing images. Convolutional Neural Networks
(CNNs) exert dominance in the cloud removal tasks. However, constrained by the
inherent limitations of convolutional operations, CNNs can address only a
modest fraction of cloud occlusion. In recent years, diffusion models have
achieved state-of-the-art (SOTA) proficiency in image generation and
reconstruction due to their formidable generative capabilities. Inspired by the
rapid development of diffusion models, we first present an iterative diffusion
process for cloud removal (IDF-CR), which exhibits a strong generative
capabilities to achieve component divide-and-conquer cloud removal. IDF-CR
consists of a pixel space cloud removal module (Pixel-CR) and a latent space
iterative noise diffusion network (IND). Specifically, IDF-CR is divided into
two-stage models that address pixel space and latent space. The two-stage model
facilitates a strategic transition from preliminary cloud reduction to
meticulous detail refinement. In the pixel space stage, Pixel-CR initiates the
processing of cloudy images, yielding a suboptimal cloud removal prior to
providing the diffusion model with prior cloud removal knowledge. In the latent
space stage, the diffusion model transforms low-quality cloud removal into
high-quality clean output. We refine the Stable Diffusion by implementing
ControlNet. In addition, an unsupervised iterative noise refinement (INR)
module is introduced for diffusion model to optimize the distribution of the
predicted noise, thereby enhancing advanced detail recovery. Our model performs
best with other SOTA methods, including image reconstruction and optical
remote-sensing cloud removal on the optical remote-sensing datasets.
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关键词
Remote-sensing image,cloud removal,diffusion model,iterative noise refinement
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