用扩散模型实现单张图像雨滴去除，效果优于传统方法。
Unsupervised Raindrop Removal from a Single Image using Conditional Diffusion Models
- 基于条件扩散模型进行图像修复，无需标注数据
- 在真实雨滴图像上实现高质量去雨效果
- 适合自动驾驶、监控等视觉系统场景
雨滴去除是图像处理中的挑战性任务。仅依赖单张图像进行去雨进一步增加了难度。常见方法是先检测图像中的雨滴区域，再基于这些区域进行背景修复。尽管检测步骤可采用多种方法，但背景修复通常使用生成对抗网络（GAN）。近年来，扩散模型在图像修复领域取得突破，达到顶尖水平。本文提出一种基于扩散模型的单图雨滴去除新方法，利用扩散模型进行图像修复，有效恢复被雨滴遮挡的背景内容。
原文摘要 · Abstract (English)
Raindrop removal is a challenging task in image processing. Removing raindrops while relying solely on a single image further increases the difficulty of the task. Common approaches include the detection of raindrop regions in the image, followed by performing a background restoration process conditioned on those regions. While various methods can be applied for the detection step, the most common architecture used for background restoration is the Generative Adversarial Network (GAN). Recent advances in the use of diffusion models have led to state-of-the-art image inpainting techniques. In this paper, we introduce a novel technique for raindrop removal from a single image using diffusion-based image inpainting.
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