Proxies for Distortion and Consistency with Applications for Real-World Image Restoration

• URL: http://arxiv.org/abs/2501.12102v1
• Date: Tue, 21 Jan 2025 12:49:30 GMT
• Title: Proxies for Distortion and Consistency with Applications for Real-World Image Restoration
• Abstract summary: This paper offers a suite of tools that can serve both the design and assessment of real-world image restoration algorithms.<n>We propose a trained model that predicts the chain of degradations a given real-world measured input has gone through.<n>We show how this estimator can be used to approximate the consistency -- the match between the measurements and any proposed recovered image.
• Score: 12.118301237297313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world image restoration deals with the recovery of images suffering from an unknown degradation. This task is typically addressed while being given only degraded images, without their corresponding ground-truth versions. In this hard setting, designing and evaluating restoration algorithms becomes highly challenging. This paper offers a suite of tools that can serve both the design and assessment of real-world image restoration algorithms. Our work starts by proposing a trained model that predicts the chain of degradations a given real-world measured input has gone through. We show how this estimator can be used to approximate the consistency -- the match between the measurements and any proposed recovered image. We also use this estimator as a guiding force for the design of a simple and highly-effective plug-and-play real-world image restoration algorithm, leveraging a pre-trained diffusion-based image prior. Furthermore, this work proposes no-reference proxy measures of MSE and LPIPS, which, without access to the ground-truth images, allow ranking of real-world image restoration algorithms according to their (approximate) MSE and LPIPS. The proposed suite provides a versatile, first of its kind framework for evaluating and comparing blind image restoration algorithms in real-world scenarios.

Related papers

• Universal Image Restoration via Internalized Chain-of-Thought Reasoning [52.763760468548504]
  Image restoration seeks to recover high-quality images from degraded inputs but becomes highly ill-posed under complex, mixed degradations.<n>Recent works adopt Chain-of-Thought (CoT) reasoning for multi-round restoration using specialized modules.<n>We introduce CoTIR, a universal image restoration framework that internalizes CoT reasoning within a single model.
  arXiv  Detail & Related papers  (2026-06-16T06:01:36Z)
• Restore, Assess, Repeat: A Unified Framework for Iterative Image Restoration [84.3817904120563]
  Image restoration aims to recover high quality images from inputs degraded by various factors, such as adverse weather, blur, or low light.<n>We propose RAR, a Restore, Assess and Repeat process, that integrates Image Quality Assessment (IQA) and Image Restoration (IR) into a unified framework.<n> Specifically, we introduce a restoration process that operates entirely in the latent domain to jointly perform degradation identification, image restoration, and quality verification.
  arXiv  Detail & Related papers  (2026-03-27T13:13:44Z)
• RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models [55.40883650570416]
  We construct a large-scale dataset covering nine common real-world degradation types.<n>We train a state-of-the-art open-source model to narrow the gap with closed-source alternatives.<n>Our model ranks first among open-source methods, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-03-26T14:39:39Z)
• From Physical Degradation Models to Task-Aware All-in-One Image Restoration [44.45223512440674]
  All-in-one image restoration aims to adaptively handle multiple restoration tasks with a single trained model.<n>We adopt a physical degradation modeling perspective and predict a task-aware inverse degradation operator for efficient all-in-one image restoration.
  arXiv  Detail & Related papers  (2026-01-15T08:47:10Z)
• 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 degradationspecific restoration.
  arXiv  Detail & Related papers  (2024-12-28T14:09:08Z)
• A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends [67.43992456058541]
  Image restoration (IR) aims to recover high-quality images from inputs degraded by various factors such as noise, blur, compression, and adverse weather.<n>Traditional IR methods typically focus on specific types of degradation, which limits their effectiveness in real-world scenarios with complex distortions.<n>The all-in-one image restoration paradigm has recently emerged, offering a unified framework that adeptly addresses multiple degradation types.
  arXiv  Detail & Related papers  (2024-10-19T11:11:09Z)
• Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction [13.277067849874756]
  We study how DIP recovers information from undersampled imaging measurements. We introduce a self-driven reconstruction process that concurrently optimize both the network weights and the input. Our method incorporates a novel denoiser regularization term which enables robust and stable joint estimation of both the network input and reconstructed image.
  arXiv  Detail & Related papers  (2024-02-06T15:52:23Z)
• Unified-Width Adaptive Dynamic Network for All-In-One Image Restoration [50.81374327480445]
  We introduce a novel concept positing that intricate image degradation can be represented in terms of elementary degradation. We propose the Unified-Width Adaptive Dynamic Network (U-WADN), consisting of two pivotal components: a Width Adaptive Backbone (WAB) and a Width Selector (WS) The proposed U-WADN achieves better performance while simultaneously reducing up to 32.3% of FLOPs and providing approximately 15.7% real-time acceleration.
  arXiv  Detail & Related papers  (2024-01-24T04:25:12Z)
• 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)
• RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images [81.91416537019835]
  We propose a novel framework, RestoreDet, to detect objects in degraded low resolution images. Our framework based on CenterNet has achieved superior performance compared with existing methods when facing variant degradation situations.
  arXiv  Detail & Related papers  (2022-01-07T03:40:23Z)
• Perceptual Image Restoration with High-Quality Priori and Degradation Learning [28.93489249639681]
  We show that our model performs well in measuring the similarity between restored and degraded images. Our simultaneous restoration and enhancement framework generalizes well to real-world complicated degradation types.
  arXiv  Detail & Related papers  (2021-03-04T13:19:50Z)
• Generative and Discriminative Learning for Distorted Image Restoration [22.230017059874445]
  Liquify is a technique for image editing, which can be used for image distortion. We propose a novel generative and discriminative learning method based on deep neural networks.
  arXiv  Detail & Related papers  (2020-11-11T14:01:29Z)
• ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms [29.864637081333086]
  This work introduces the ENsemble Stein's Unbiased Risk Estimate (ENSURE) framework as a general approach to train deep image reconstruction algorithms. We show that the ENSURE loss function, which only uses the measurement data, is an unbiased estimate for the true mean-square error. Our experiments show that the networks trained with this loss function can offer reconstructions comparable to the supervised setting.
  arXiv  Detail & Related papers  (2020-10-20T21:18:33Z)
• Structured and Localized Image Restoration [141.75042935077465]
  We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning. We derive the corresponding algorithms for energies based on the mean-squared and Euclidean norm errors.
  arXiv  Detail & Related papers  (2020-06-16T15:43:12Z)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)

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.