论文标题
QMRNET：EO图像质量评估和超分辨率的质量度量回归
QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution
论文作者
论文摘要
超分辨率（SR）的最新进展已通过通用图像（例如面部，景观和物体）进行了测试，主要未用于超级分辨地球观测（EO）图像的任务。在这篇研究论文中，我们使用全参考和无参考图像质量评估（IQA）指标基准了针对不同EO数据集的最新SR算法。我们还提出了一个新颖的质量度量回归网络（QMRNET），能够通过对图像的任何属性进行培训（即其分辨率，其扭曲...）来预测质量（作为无参考度量指标），并且还能够优化SR算法的特定度量目标。这项工作是框架IQUAFLOW实施的一部分，该框架是为了评估对象的图像质量，检测和分类以及EO用例中的图像压缩的开发的。我们整合了实验，并测试了QMRNET算法在预测诸如模糊，清晰度，SNR，RER和地面采样距离（GSD）之类的特征上，并获得低于1.0（n = 50）的验证MEDR，并召回95 \％以上的召回率。总体基准测试了LIIF，CAR和MSRN的有希望的结果，以及QMRNET作为优化SR预测的损失的潜在用途。由于其简单性，QMRNET也可以用于其他用例和图像域，因为其架构和数据处理是完全可扩展的。
Latest advances in Super-Resolution (SR) have been tested with general purpose images such as faces, landscapes and objects, mainly unused for the task of super-resolving Earth Observation (EO) images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both Full-Reference and No-Reference Image Quality Assessment (IQA) metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict quality (as a No-Reference metric) by training on any property of the image (i.e. its resolution, its distortions...) and also able to optimize SR algorithms for a specific metric objective. This work is part of the implementation of the framework IQUAFLOW which has been developed for evaluating image quality, detection and classification of objects as well as image compression in EO use cases. We integrated our experimentation and tested our QMRNet algorithm on predicting features like blur, sharpness, snr, rer and ground sampling distance (GSD) and obtain validation medRs below 1.0 (out of N=50) and recall rates above 95\%. Overall benchmark shows promising results for LIIF, CAR and MSRN and also the potential use of QMRNet as Loss for optimizing SR predictions. Due to its simplicity, QMRNet could also be used for other use cases and image domains, as its architecture and data processing is fully scalable.
