Fine-grained Image Quality Assessment for Perceptual Image Restoration
arXiv:2508.14475v4 Announce Type: replace Abstract: Recent years have witnessed remarkable achievements in perceptual image restoration (IR), creating an urgent demand for accurate image quality assessment (IQA), which is essential for both performance comparison and algorithm optimization. Unfortunately, the existing IQA metrics exhibit inherent weakness for IR task, particularly when distinguishing fine-grained quality differences among restored images. To address this dilemma, we contribute the first-of-its-kind fine-grained image quality assessment dataset for image restoration, termed FGRestore, comprising 18,408 restored images across six common IR tasks. Beyond conventional scalar quality scores, FGRestore was also annotated with 30,886 fine-grained pairwise preferences. Based on FG
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Abstract:Recent years have witnessed remarkable achievements in perceptual image restoration (IR), creating an urgent demand for accurate image quality assessment (IQA), which is essential for both performance comparison and algorithm optimization. Unfortunately, the existing IQA metrics exhibit inherent weakness for IR task, particularly when distinguishing fine-grained quality differences among restored images. To address this dilemma, we contribute the first-of-its-kind fine-grained image quality assessment dataset for image restoration, termed FGRestore, comprising 18,408 restored images across six common IR tasks. Beyond conventional scalar quality scores, FGRestore was also annotated with 30,886 fine-grained pairwise preferences. Based on FGRestore, a comprehensive benchmark was conducted on the existing IQA metrics, which reveal significant inconsistencies between score-based IQA evaluations and the fine-grained restoration quality. Motivated by these findings, we further propose FGResQ, a new IQA model specifically designed for image restoration, which features both coarse-grained score regression and fine-grained quality ranking. Extensive experiments and comparisons demonstrate that FGResQ significantly outperforms state-of-the-art IQA metrics. Codes and model weights have been released in this https URL.
Comments: Accepted by AAAI2026
Subjects:
Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
Cite as: arXiv:2508.14475 [eess.IV]
(or arXiv:2508.14475v4 [eess.IV] for this version)
https://doi.org/10.48550/arXiv.2508.14475
arXiv-issued DOI via DataCite
Submission history
From: Pan Xiaofeng [view email] [v1] Wed, 20 Aug 2025 06:58:32 UTC (7,910 KB) [v2] Tue, 2 Sep 2025 08:52:32 UTC (7,911 KB) [v3] Mon, 17 Nov 2025 02:08:08 UTC (7,910 KB) [v4] Tue, 31 Mar 2026 08:20:40 UTC (6,120 KB)
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