Papers › Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models
Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models
Ozan Özdenizci, Robert Legenstein
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.
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Code
Syntology Ran 10 of 11 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 3 ran · our draft was wrong; 2 ran · fixture could not drive it; 3 ran with no contract checked.
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Code Syntology ran Syntology
11 samples harvested; 10 ran; 1 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Restoration | CDD-11 | WeatherDiff | Average PSNR (dB) | 22.49 | #14 of 14 | Archive leaderboard | report |
| Image Restoration | CDD-11 | WeatherDiff | SSIM | 0.7985 | #14 of 14 | Archive leaderboard | report |
| Rain Removal | Nightrain | WeatherDiff | PSNR | 20.98 | #4 of 4 | Archive leaderboard | report |
| Single Image Deraining | Raindrop | RainDropDiff128 | PSNR | 32.43 | #3 of 3 | Archive leaderboard | report |
| Single Image Deraining | Raindrop | RainDropDiff128 | SSIM | 0.933 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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