UniRes: Universal Image Restoration for Complex Degradations

• URL: http://arxiv.org/abs/2506.05599v1
• Date: Thu, 05 Jun 2025 21:25:39 GMT
• Title: UniRes: Universal Image Restoration for Complex Degradations
• Authors: Mo Zhou, Keren Ye, Mauricio Delbracio, Peyman Milanfar, Vishal M. Patel, Hossein Talebi,
• Abstract summary: 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.
• Score: 53.74404005987783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world image restoration is hampered by diverse degradations stemming from varying capture conditions, capture devices and post-processing pipelines. Existing works make improvements through simulating those degradations and leveraging image generative priors, however generalization to in-the-wild data remains an unresolved problem. In this paper, we focus on complex degradations, i.e., arbitrary mixtures of multiple types of known degradations, which is frequently seen in the wild. A simple yet flexible diffusionbased framework, named UniRes, is proposed to address such degradations in an end-to-end manner. It combines several specialized models during the diffusion sampling steps, hence transferring the knowledge from several well-isolated restoration tasks to the restoration of complex in-the-wild degradations. This only requires well-isolated training data for several degradation types. The framework is flexible as extensions can be added through a unified formulation, and the fidelity-quality trade-off can be adjusted through a new paradigm. Our proposed method is evaluated on both complex-degradation and single-degradation image restoration datasets. Extensive qualitative and quantitative experimental results show consistent performance gain especially for images with complex degradations.

Related papers

• UniUIR: Considering Underwater Image Restoration as An All-in-One Learner [49.35128836844725]
  We propose a Universal Underwater Image Restoration method, termed as UniUIR.<n>To decouple degradation-specific issues and explore the inter-correlations among various degradations in UIR task, we designed the Mamba Mixture-of-Experts module.<n>This module extracts degradation prior information in both spatial and frequency domains, and adaptively selects the most appropriate task-specific prompts.
  arXiv  Detail & Related papers  (2025-01-22T16:10:42Z)
• UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity [79.90839080916913]
  We present our UniRestorer with improved restoration performance.<n>Specifically, we perform hierarchical clustering on degradation space, and train a multi-granularity mixture-of-experts (MoE) restoration model.<n>In contrast to existing degradation-agnostic and -aware methods, UniRestorer can leverage degradation estimation to benefit degradation specific restoration.
  arXiv  Detail & Related papers  (2024-12-28T14:09:08Z)
• Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding [67.57487747508179]
  Multiple-in-one image restoration (IR) has made significant progress, aiming to handle all types of single degraded image restoration with a single model. In this paper, we propose a novel multiple-in-one IR model that can effectively restore images with both single and mixed degradations.
  arXiv  Detail & Related papers  (2024-11-25T09:26:34Z)
• Chain-of-Restoration: Multi-Task Image Restoration Models are Zero-Shot Step-by-Step Universal Image Restorers [53.298698981438]
  This paper proposes a new task setting, i.e. Universal Image Restoration (UIR)<n>UIR doesn't require training on all the degradation combinations but only on a set of degradation bases and then removing any degradation that these bases can potentially compose in a zero-shot manner.<n>We propose Chain-of-Restoration (CoR) mechanism, which instructs models to remove unknown composite degradations step-by-step.
  arXiv  Detail & Related papers  (2024-10-11T10:21:42Z)
• OneRestore: A Universal Restoration Framework for Composite Degradation [33.556183375565034]
  In real-world scenarios, image impairments often manifest as composite degradations, presenting a complex interplay of elements such as low light, haze, rain, and snow. Our study proposes a versatile imaging model that consolidates four physical corruption paradigms to accurately represent complex, composite degradation scenarios. OneRestore is a novel transformer-based framework designed for adaptive, controllable scene restoration.
  arXiv  Detail & Related papers  (2024-07-05T16:27:00Z)
• 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)
• 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)
• 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)
• Invertible Rescaling Network and Its Extensions [118.72015270085535]
  In this work, we propose a novel invertible framework to model the bidirectional degradation and restoration from a new perspective. We develop invertible models to generate valid degraded images and transform the distribution of lost contents. Then restoration is made tractable by applying the inverse transformation on the generated degraded image together with a randomly-drawn latent variable.
  arXiv  Detail & Related papers  (2022-10-09T06:58:58Z)

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.