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

2025-01-21

AI Summary

Addressing blind image restoration under unknown real-world degradations without ground-truth images, this work proposes a holistic solution. First, we introduce a learnable degradation chain estimator to accurately model complex, realistic degradations. Second, we design a consistency-driven, plug-and-play diffusion prior framework enabling end-to-end lightweight optimization. Third, we pioneer reference-free proxy metrics—MSE and LPIPS computed on synthetically degraded samples—that overcome the longstanding challenge of unreliable performance evaluation in blind restoration. To our knowledge, this is the first work unifying degradation modeling, restoration algorithm design, and no-reference assessment within a single coherent pipeline. Extensive experiments demonstrate substantial improvements in ranking accuracy over SOTA methods across multiple blind restoration benchmarks, significantly enhancing algorithmic assessability, comparability, and practical applicability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Image Restoration
• Unknown Degradation
• Algorithm Evaluation

Innovation

• Image Restoration Algorithms
• Quality Evaluation Methods
• Pre-learned Image Knowledge