论文标题
基于学习的图像超分辨率的质量评估
Learning-Based Quality Assessment for Image Super-Resolution
论文作者
论文摘要
图像超分辨率（SR）技术通过增强图像的空间分辨率来改善视觉质量。质量评估指标在比较和优化SR算法中起着至关重要的作用，但是当前的指标仅取得了有限的成功，这主要是由于缺乏大规模质量数据库，这对于学习准确且健壮的SR质量指标至关重要。在这项工作中，我们首先使用一种新型的半自动标签方法构建了一个大规模的SR图像数据库，该方法使我们能够用可管理的人类工作量标记大量图像。迄今为止最大的SR-IQA数据库中，带有半自动评分（SISAR）的SR图像质量数据库包含100个自然场景的8,400张图像。我们通过使用两流深神经网络（DNN）进行特征提取，然后使用用于质量预测的功能融合网络来训练端到端的深层图像SR质量（DISQ）模型。实验结果表明，所提出的方法优于最先进的指标，并且在跨数据库测试中实现了有希望的概括性能。 SISAR数据库和DISQ模型将公开使用，以促进可重复的研究。
Image Super-Resolution (SR) techniques improve visual quality by enhancing the spatial resolution of images. Quality evaluation metrics play a critical role in comparing and optimizing SR algorithms, but current metrics achieve only limited success, largely due to the lack of large-scale quality databases, which are essential for learning accurate and robust SR quality metrics. In this work, we first build a large-scale SR image database using a novel semi-automatic labeling approach, which allows us to label a large number of images with manageable human workload. The resulting SR Image quality database with Semi-Automatic Ratings (SISAR), so far the largest of SR-IQA database, contains 8,400 images of 100 natural scenes. We train an end-to-end Deep Image SR Quality (DISQ) model by employing two-stream Deep Neural Networks (DNNs) for feature extraction, followed by a feature fusion network for quality prediction. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The SISAR database and DISQ model will be made publicly available to facilitate reproducible research.
