提出一套无需真值图像的图像修复评估与设计工具
Proxies for Distortion and Consistency with Applications for Real-World Image Restoration
- 用训练模型预测真实图像退化链，指导修复算法设计
- 实现无参考的MSE和LPIPS近似度量，可排序修复算法性能
- 首次提供盲修复算法在真实场景下的完整评估框架
真实世界图像修复面临未知退化问题，通常仅能获得退化图像而无对应真值。本文提出一套工具，用于设计和评估此类算法。首先构建一个训练模型，可预测给定真实测量图像所经历的退化链；利用该估计器近似一致性（即测量与恢复图像之间的匹配程度）。进一步地，将其作为引导，结合预训练扩散图像先验，设计出一种简单高效的即插即用修复算法。此外，本文还提出无需参考图像的MSE与LPIPS代理指标，可在无真值情况下对修复算法进行性能排序。该套工具构成首个面向真实场景下盲图像修复算法的通用评估与比较框架。
原文摘要 · Abstract (English)
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.
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