提出无需训练的深度结构相似性方法，提升图像质量评估对几何失真的鲁棒性。
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference
- 通过深层特征结构相似性计算，不依赖特定任务设计。
- 在标准数据集上表现超越现有方法，且对多种几何变形均有效。
- 适用于超分辨率、增强等任务，具备广泛通用性。
带参考的图像质量评估（IQA）在优化和评价计算机视觉任务中具有重要意义。传统方法假设参考图与测试图所有像素完全对齐，这类对齐参考IQA（AR-IQA）方法难以应对真实场景中普遍存在的几何形变问题。尽管已有研究尝试解决几何非对齐参考IQA（GDR-IQA）问题，但多采用任务特异性设计，如针对图像超分辨率或重缩放的定制方案，或假设形变较小可由平移鲁棒滤波器补偿，或依赖显式图像配准。本文重新思考该问题，提出统一的、无需训练的深度结构相似性（DeepSSIM）方法，在单一框架内高效评估深层特征的结构相似性，并引入注意力校准策略缓解注意力偏差。所提方法无需任务特化设计，在AR-IQA数据集上达到顶尖性能，同时对多种GDR-IQA测试情形表现出强鲁棒性。有趣的是，实验还表明DeepSSIM可作为图像超分辨率、增强与修复任务的优化工具，暗示其更广的泛化能力。源代码将在评审完成后公开。
原文摘要 · Abstract (English)
Image Quality Assessment (IQA) with references plays an important role in optimizing and evaluating computer vision tasks. Traditional methods assume that all pixels of the reference and test images are fully aligned. Such Aligned-Reference IQA (AR-IQA) approaches fail to address many real-world problems with various geometric deformations between the two images. Although significant effort has been made to attack Geometrically-Disparate-Reference IQA (GDR-IQA) problem, it has been addressed in a task-dependent fashion, for example, by dedicated designs for image super-resolution and retargeting, or by assuming the geometric distortions to be small that can be countered by translation-robust filters or by explicit image registrations. Here we rethink this problem and propose a unified, non-training-based Deep Structural Similarity (DeepSSIM) approach to address the above problems in a single framework, which assesses structural similarity of deep features in a simple but efficient way and uses an attention calibration strategy to alleviate attention deviation. The proposed method, without application-specific design, achieves state-of-the-art performance on AR-IQA datasets and meanwhile shows strong robustness to various GDR-IQA test cases. Interestingly, our test also shows the effectiveness of DeepSSIM as an optimization tool for training image super-resolution, enhancement and restoration, implying an even wider generalizability. \footnote{Source code will be made public after the review is completed.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。