Source-linked AI summary
Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models
Ozan Özdenizci, Robert Legenstein
TL;DR
Adverse-weather image restoration is difficult because weather can obscure scene content and diffusion architectures do not inherently support arbitrary image sizes. The paper introduces patch-based conditional diffusion restoration with guided denoising across overlapping patches, achieving state-of-the-art results across weather-specific and multi-weather benchmarks and strong real-world generalization. Its main trade-off is longer inference time, and it does not generalize conditionally to corruptions unseen during training.
Problem
Adverse-weather restoration is an ill-posed inverse problem, while diffusion architectures impose constraints that hinder size-agnostic processing of varied benchmark and real-world images.
Method
The paper uses conditional denoising diffusion probabilistic models with overlapping patch extraction and guided reverse sampling based on smoothed noise estimates.
Results
The approach achieves state-of-the-art performance on weather-specific and multi-weather restoration tasks and shows strong generalization from synthetic to real-world images.
Takeaways & Limitations
Patch-based processing makes diffusion restoration applicable to arbitrary-sized images while reducing computational demands enough to run on a single GPU with as little as 12GB memory.
Takeaways & Limitations
Inference is substantially slower than single-pass restoration networks, and conditional generalization remains limited to corruptions observed during training.
Abstract
from arXiv · showhide
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.
1 INTRODUCTION
Adverse-weather restoration is an ill-posed vision problem, and existing diffusion models face size constraints that limit their direct use. The paper introduces patch-based diffusion restoration and reports strong performance across weather-specific, multi-weather, and real-world settings.
- Motivation: Adverse weather can severely obscure the true background, making single-image restoration an ill-posed inverse problem.Multiple background solutions may explain the same weather-degraded observation.
- Motivation: Prior deep-learning work explored task-specific generative models, including GANs, with potential for multi-task restoration generalization.These models learn cleared-image background distributions from weather-degraded training examples.
- Research gap: Diffusion models had not yet been applied to adverse-weather restoration or demonstrated cross-task generalization, while their architecture restricted size-agnostic processing.Benchmarks and real-world images can have varying sizes, creating a practical mismatch.
- Contribution: The proposed patch-based diffusion method enables arbitrary-sized image restoration through guided denoising over overlapping patches.The approach steers sampling using smoothed noise estimates across patches.
- Results: The method achieves state-of-the-art performance on weather-specific and multi-weather restoration tasks and shows strong generalization from synthetic to real-world images.Evaluations cover desnowing, combined deraining and dehazing, and raindrop removal.
2 RELATED WORK
The related work spans diffusion and score-based generative modeling, conventional and deep weather restoration, and unified multi-weather systems. These strands motivate applying conditional generative modeling to weather-degraded image restoration.
- Generative modeling: Diffusion and score-based models learn to reverse sequential corruption by increasing additive noise, enabling generative sampling.The passage situates denoising diffusion probabilistic models and noise-conditional score networks within this model class.
- Generative modeling: Energy-based models offer a related sampling perspective but differ through contrastive-divergence training and Langevin dynamics.They estimate an unnormalized Boltzmann probability density using a learned energy function.
- Conditional generative models: Conditional diffusion models have achieved state-of-the-art results in synthesis, super-resolution, deblurring, and image-to-image translation.Related score-based conditional applications have also been developed.
- Weather restoration: Weather restoration is an ill-posed inverse problem because observed pixels mix weather distortion with background content that may be fully occluded.Traditional methods addressed this challenge using priors based on weather-distortion characteristics.
- Weather restoration: Prior task-specific restoration research addressed deraining and dehazing with CNNs, recurrent context aggregation, spatial attention, and GAN-based translation.These approaches established several architectural directions for removing rain-related degradations.
- Weather restoration: Related work separately studied raindrop removal, image desnowing, and unified multi-weather restoration with specialized and multi-task frameworks.Unified systems include methods for superimposed noise, unknown mixtures, and adverse multi-weather degradations.
3 ADVERSE WEATHER IMAGE RESTORATION WITH PATCH-BASED DENOISING DIFFUSION MODELS
The paper adapts conditional denoising diffusion models for arbitrary-sized adverse-weather image restoration by operating on overlapping patches and guiding reverse sampling with smoothed noise estimates. The approach addresses patch-boundary inconsistencies while retaining conditional diffusion sampling.
- Denoising diffusion models: Denoising diffusion models learn a Markov chain that converts Gaussian noise into the training data distribution through learned reverse denoising transitions.The forward process progressively corrupts data with Gaussian noise, while the reverse process starts from a standard normal prior.
- Conditional diffusion models: Conditional diffusion models learn a reverse process conditioned on a degraded image, producing samples with fidelity to that condition.Training uses paired clean and weather-degraded images, with the degraded image supplied to the reverse process.
- Patch-based restoration: The patch-based method decomposes arbitrary-sized degraded images into overlapping p × p patches extracted on a grid with step size r.The grid contains r × r cells with r < p, and all extracted patches are represented in a dictionary of overlapping locations.
- Patch-based restoration: Averaging final reconstructions after sampling is distinguished from guided sampling because post-sampling averaging destroys local patch fidelity to the learned posterior.The proposed method performs smoothing during the reverse process instead of merging independently restored outputs only at the end.
- Multi-weather restoration: The task-agnostic conditional diffusion approach is designed to handle multiple weather corruptions when paired examples from mixed degradations are available during training.The model estimates the background while restoring images containing occlusions such as large snowflakes or raindrops.
4 EXPERIMENTAL RESULTS
Experiments evaluate weather-specific and unified multi-weather diffusion models across synthetic benchmarks and real-world images. The models achieve strong quantitative and qualitative restoration results across desnowing, combined deraining and dehazing, and raindrop removal.
- Experimental Setup: The study evaluates weather-specific and multi-weather restoration models on snow, simultaneous rain and fog, and raindrop benchmarks.Weather-specific models are SnowDiffp, RainHazeDiffp, and RainDropDiffp; WeatherDiffp is the unified model.
- Weather-Specific Results: The weather-specific models outperform all compared existing methods across the evaluated restoration tasks.For desnowing and combined deraining and dehazing, 64x64 models achieve 36.59/0.9626 on Snow100K-S, 30.43/0.9145 on Snow100K-L, and 28.38/0.9320 on Outdoor-Rain; RainDropDiff128 reaches a best PSNR of 32.52.
- Weather-Specific Results: Qualitative comparisons show fine-detail preservation for desnowing, recovery of dehazing-lost structures, and more ground-truth-like raindrop removal than competing methods.Examples highlight fine details, metal railing lines, and reconstructions compared with GAN-based approaches.
- Multi-Weather Results: WeatherDiff64 achieves 35.83/0.9566 and 30.09/0.9041 on Snow100K-S and Snow100K-L, while WeatherDiff64/128 reach 29.64/29.72 PSNR on Outdoor-Rain.The combined deraining and dehazing values outperform the dedicated weather-specific models reported in the comparison.
- Multi-Weather Results: WeatherDiff models lose to All-in-One in only one reported metric, RainDrop PSNR, while achieving higher RainDrop SSIM.All-in-One records 31.12 PSNR and 0.9268 SSIM versus 30.71 and 0.9312 for the authors’ models.
- Real-World Generalization: On real-world restoration examples, WeatherDiff preserves or recovers details that TransWeather sometimes removes, and obtains better perceptual quality scores on both test sets.The real-world evaluation includes snowy images and raindrop images; WeatherDiff64 and WeatherDiff128 significantly outperform TransWeather on the reported reference-free metrics.
5 DISCUSSION
The paper presents conditional diffusion restoration that processes arbitrary-sized images through patches, achieving strong weather-restoration performance while extending practical deployment. Its empirical analyses use default diffusion architectures, and it remains limited in inference speed and generalization to unseen corruptions.
- The proposed conditional diffusion approach achieves state-of-the-art performance on weather-specific and multi-weather restoration tasks.
- Patch-based processing makes the model input size-agnostic and supports lighter diffusion backbones for lower-resolution restoration.The approach enables restoration on a single GPU with as little as 12GB memory and permits resolution-specific attention mechanisms.
- The empirical analyses primarily use default architectural and parameter choices from seminal diffusion-modeling works.The authors suggest improved sample-quality and faster-sampling methods could further improve quantitative results on particular weather-restoration problems.
- 5.1 Limitations: 20.52 seconds versus 0.88 seconds: WeatherDiff64 requires substantially longer inference than TransWeather on a 640 × 432 image.The timing depends on algorithm hyperparameters and implementation efficiency.
- 5.1 Limitations: The model generalizes only to restoration tasks observed during training and does not conditionally generalize to unseen corruptions such as poor lighting.Multi-weather restoration is enabled by training on image pairs from multiple observed weather degradations.