标题： 细粒度图像质量评估用于感知图像修复 显示英文标题

标题： Fine-grained Image Quality Assessment for Perceptual Image Restoration

摘要： 近年来，感知图像修复（IR）取得了显著的成就，这迫切需要准确的图像质量评估（IQA），这对于性能比较和算法优化至关重要。 不幸的是，现有的IQA度量在IR任务中表现出固有的弱点，特别是在区分修复图像之间的细微质量差异时。 为了解决这一困境，我们贡献了第一个针对图像修复的细粒度图像质量评估数据集，称为FGRestore，包含六个常见IR任务中的18,408张修复图像。 除了传统的标量质量分数， FGRestore还标注了30,886个细粒度的成对偏好。 基于FGRestore，对现有的IQA度量进行了全面的基准测试，结果揭示了基于分数的IQA评估与细粒度修复质量之间存在显著不一致。 受这些发现的启发，我们进一步提出了FGResQ，一种专门为图像修复设计的新IQA模型，该模型同时具备粗粒度分数回归和细粒度质量排序。 大量实验和比较表明，FGResQ显著优于最先进的IQA度量。 代码和模型权重已发布在https://pxf0429.github.io/FGResQ/ 显示英文摘要

摘要： 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://pxf0429.github.io/FGResQ/