ReviveDiff: A Universal Diffusion Model for Restoring Images in Adverse Weather Conditions

• URL: http://arxiv.org/abs/2409.18932v4
• Date: Mon, 03 Nov 2025 12:31:34 GMT
• Title: ReviveDiff: A Universal Diffusion Model for Restoring Images in Adverse Weather Conditions
• Abstract summary: We propose a universal network architecture, dubbed ReviveDiff'', which can address various degradations and bring images back to life.<n>Our approach is inspired by the observation that quality degradation under adverse conditions primarily stems from natural media.<n>We rigorously evaluated ReviveDiff on seven benchmark datasets covering five types of degrading conditions.
• Score: 15.288300322235473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Images captured in challenging environments--such as nighttime, smoke, rainy weather, and underwater--often suffer from significant degradation, resulting in a substantial loss of visual quality. The effective restoration of these degraded images is critical for the subsequent vision tasks. While many existing approaches have successfully incorporated specific priors for individual tasks, these tailored solutions limit their applicability to other degradations. In this work, we propose a universal network architecture, dubbed ``ReviveDiff'', which can address various degradations and bring images back to life by enhancing and restoring their quality. Our approach is inspired by the observation that, unlike degradation caused by movement or electronic issues, quality degradation under adverse conditions primarily stems from natural media (such as fog, water, and low luminance), which generally preserves the original structures of objects. To restore the quality of such images, we leveraged the latest advancements in diffusion models and developed ReviveDiff to restore image quality from both macro and micro levels across some key factors determining image quality, such as sharpness, distortion, noise level, dynamic range, and color accuracy. We rigorously evaluated ReviveDiff on seven benchmark datasets covering five types of degrading conditions: Rainy, Underwater, Low-light, Smoke, and Nighttime Hazy. Our experimental results demonstrate that ReviveDiff outperforms the state-of-the-art methods both quantitatively and visually.

Related papers

• All-in-One Video Restoration under Smoothly Evolving Unknown Weather Degradations [102.94052335735326]
  All-in-one image restoration aims to recover clean images from diverse unknown degradations using a single model.<n>Existing approaches primarily focus on frame-wise degradation variation, overlooking the temporal continuity that naturally exists in real-world degradation processes.<n>We introduce the Smoothly Evolving Unknown Degradations (SEUD) scenario, where both the active degradation set and degradation intensity change continuously over time.
  arXiv  Detail & Related papers  (2026-01-02T02:20:57Z)
• TDiR: Transformer based Diffusion for Image Restoration Tasks [19.992144590243836]
  Images captured in challenging environments often experience various forms of degradation, including noise, color cast, blur, and light scattering.<n>These effects significantly reduce image quality, hindering their applicability in downstream tasks such as object detection, mapping, and classification.<n>Our transformer-based diffusion model was developed to address image restoration tasks, aiming to improve the quality of degraded images.
  arXiv  Detail & Related papers  (2025-06-25T10:28:13Z)
• UniRes: Universal Image Restoration for Complex Degradations [53.74404005987783]
  Real-world image restoration is hampered by diverse degradations stemming from varying capture conditions, capture devices and post-processing pipelines.<n>A simple yet flexible diffusionbased framework, named UniRes, is proposed to address such degradations in an end-to-end manner.<n>Our proposed method is evaluated on both complex-degradation and single-degradation image restoration datasets.
  arXiv  Detail & Related papers  (2025-06-05T21:25:39Z)
• Degradation-Aware Image Enhancement via Vision-Language Classification [12.72311942967158]
  We propose a framework that employs a Vision-Language Model (VLM) to automatically classify degraded images into predefined categories.<n>The VLM categorizes an input image into one of four degradation types: (A) super-resolution degradation (including noise, blur, and JPEG compression), (B) reflection artifacts, (C) motion blur, or (D) no visible degradation.<n>Once classified, images assigned to categories A, B, or C undergo targeted restoration using dedicated models tailored for each specific degradation type.
  arXiv  Detail & Related papers  (2025-06-05T17:42:01Z)
• Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration [19.87693298262894]
  We propose Diff-Restorer, a universal image restoration method based on the diffusion model. We utilize the pre-trained visual language model to extract visual prompts from degraded images. We also design a Degradation-aware Decoder to perform structural correction and convert the latent code to the pixel domain.
  arXiv  Detail & Related papers  (2024-07-04T05:01:10Z)
• Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models [14.25759541950917]
  This work leverages a capable vision-language model and a synthetic degradation pipeline to learn image restoration in the wild (wild IR) Our base diffusion model is the image restoration SDE (IR-SDE)
  arXiv  Detail & Related papers  (2024-04-15T12:34:21Z)
• Joint Conditional Diffusion Model for Image Restoration with Mixed Degradations [29.14467633167042]
  We propose a new method for image restoration in adverse weather conditions. We use a mixed degradation model based on atmosphere scattering model to guide the whole restoration process. Experiments on both multi-weather and weather-specific datasets demonstrate the superiority of our method over state-of-the-art competing methods.
  arXiv  Detail & Related papers  (2024-04-11T14:07:16Z)
• AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation [99.57024606542416]
  We propose an adaptive all-in-one image restoration network based on frequency mining and modulation. Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands. The proposed model achieves adaptive reconstruction by accentuating the informative frequency subbands according to different input degradations.
  arXiv  Detail & Related papers  (2024-03-21T17:58:14Z)
• InstructIR: High-Quality Image Restoration Following Human Instructions [61.1546287323136]
  We present the first approach that uses human-written instructions to guide the image restoration model. Our method, InstructIR, achieves state-of-the-art results on several restoration tasks.
  arXiv  Detail & Related papers  (2024-01-29T18:53:33Z)
• All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation [47.00239809958627]
  We propose a novel All-in-one Multi-degradation Image Restoration Network (AMIRNet) AMIRNet learns a degradation representation for unknown degraded images by progressively constructing a tree structure through clustering. This tree-structured representation explicitly reflects the consistency and discrepancy of various distortions, providing a specific clue for image restoration.
  arXiv  Detail & Related papers  (2023-08-06T04:51:41Z)
• PromptIR: Prompting for All-in-One Blind Image Restoration [64.02374293256001]
  We present a prompt-based learning approach, PromptIR, for All-In-One image restoration. Our method uses prompts to encode degradation-specific information, which is then used to dynamically guide the restoration network. PromptIR offers a generic and efficient plugin module with few lightweight prompts.
  arXiv  Detail & Related papers  (2023-06-22T17:59:52Z)
• DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration [66.01846902242355]
  Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training. It is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. We propose Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-invariant prediction, then employ an enhancement module to restore the coarse prediction to a high-quality image.
  arXiv  Detail & Related papers  (2023-03-13T06:05:18Z)
• See Blue Sky: Deep Image Dehaze Using Paired and Unpaired Training Images [73.23687409870656]
  We propose a cycle generative adversarial network to construct a novel end-to-end image dehaze model. We adopt outdoor image datasets to train our model, which includes a set of real-world unpaired image dataset and a set of paired image dataset. Based on the cycle structure, our model adds four different kinds of loss function to constrain the effect including adversarial loss, cycle consistency loss, photorealism loss and paired L1 loss.
  arXiv  Detail & Related papers  (2022-10-14T07:45:33Z)
• Semi-supervised atmospheric component learning in low-light image problem [0.0]
  Ambient lighting conditions play a crucial role in determining the perceptual quality of images from photographic devices. This study presents a semi-supervised training method using no-reference image quality metrics for low-light image restoration.
  arXiv  Detail & Related papers  (2022-04-15T17:06:33Z)
• Invertible Image Rescaling [118.2653765756915]
  We develop an Invertible Rescaling Net (IRN) to produce visually-pleasing low-resolution images. We capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process.
  arXiv  Detail & Related papers  (2020-05-12T09:55:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.