针对图像修复的细粒度质量评估，提出新模型FGResQ和首个专用数据集FGRestore。
Fine-grained Image Quality Assessment for Perceptual Image Restoration
- 构建包含1.8万张修复图像和3万对细粒度偏好标注的数据集FGRestore。
- 发现现有评估指标在区分细微修复质量上存在显著偏差。
- 提出可同时做评分回归与排序的FGResQ模型，性能超越现有方法。
近年来感知图像修复（IR）取得显著进展，亟需精准的图像质量评估（IQA）以支持性能对比与算法优化。然而，现有IQA指标在区分修复图像的细粒度质量差异方面存在固有缺陷。为此，我们构建了首个面向图像修复的细粒度质量评估数据集FGRestore，涵盖六类常见修复任务的18,408张修复图像，并标注了30,886对细粒度成对偏好。基于此，我们对现有IQA指标进行了全面基准测试，揭示了基于分数的评估与细粒度质量感知之间的显著不一致。受此启发，我们进一步提出专为图像修复设计的FGResQ模型，兼具粗粒度评分回归与细粒度质量排序能力。大量实验表明，FGResQ显著优于当前最优IQA指标。代码与模型权重已公开于https://sxfly99.github.io/FGResQ-Home。
原文摘要 · Abstract (English)
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 https://sxfly99.github.io/FGResQ-Home.
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