Image restoration under adverse weather conditions has been of significant
interest for various computer vision applications. Recent successful methods
rely on the current progress in deep neural network architectural designs
(e.g., with vision transformers). Motivated by the recent progress achieved
with state-of-the-art conditional generative models, we present a novel
patch-based image restoration algorithm based on denoising diffusion
probabilistic models. Our patch-based diffusion modeling approach enables
size-agnostic image restoration by using a guided denoising process with
smoothed noise estimates across overlapping patches during inference. We
empirically evaluate our model on benchmark datasets for image desnowing,
combined deraining and dehazing, and raindrop removal. We demonstrate our
approach to achieve state-of-the-art performances on both weather-specific and
multi-weather image restoration, and experimentally show strong generalization
to real-world test images.

基于概率模型的图像修复算法，通过引导去噪过程来实现任意尺寸的图像修复。该算法在图像去雪、去雨和去雾等数据集上表现出了领先的性能，并且在实际测试图像方面具有很强的泛化性能。