论文标题
低光还原图像的质量评估：主观研究和无监督模型
Quality Assessment of Low Light Restored Images: A Subjective Study and an Unsupervised Model
论文作者
论文摘要
恢复的低光图像的质量评估（QA）是基准测试和改善低光修复（LLR）算法的重要工具。尽管存在几种LLR算法，但对恢复图像的主观感知的研究少得多。在捕获一致的低光和光线充足的图像对以及收集大量人类培训质量的挑战时，需要设计无监督的（或意见不自觉）的无参考（NR）QA方法。这项工作研究了低光修复图像的主观感知及其无监督的NR QA。我们的贡献是两个方面。我们首先使用各种LLR方法创建一个恢复的低光图像数据集，进行主观质量检查研究，并根据现有QA方法的性能进行基准测试。然后，我们提出了一种自我监管的对比学习技术，以从还原的低光图像中提取失真的意识特征。我们表明，这些功能可有效地用于构建意见不知道的图像质量分析仪。详细的实验表明，我们无监督的NR质量检查模型在所有此类质量措施中都可以在低光还原图像中实现最先进的性能。
The quality assessment (QA) of restored low light images is an important tool for benchmarking and improving low light restoration (LLR) algorithms. While several LLR algorithms exist, the subjective perception of the restored images has been much less studied. Challenges in capturing aligned low light and well-lit image pairs and collecting a large number of human opinion scores of quality for training, warrant the design of unsupervised (or opinion unaware) no-reference (NR) QA methods. This work studies the subjective perception of low light restored images and their unsupervised NR QA. Our contributions are two-fold. We first create a dataset of restored low light images using various LLR methods, conduct a subjective QA study and benchmark the performance of existing QA methods. We then present a self-supervised contrastive learning technique to extract distortion aware features from the restored low light images. We show that these features can be effectively used to build an opinion unaware image quality analyzer. Detailed experiments reveal that our unsupervised NR QA model achieves state-of-the-art performance among all such quality measures for low light restored images.
