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.
翻译:超分辨率(SR)的最新进展已经用面部、地貌和物体等一般目的图像测试了超分辨率(SR)的最新进展,这些图像主要用于超分辨率地球观测(EO)图像的任务,主要用于超分辨率地球观测(EO)图像。在本研究论文中,我们用全参考和不参考图像质量评估(IQA)的衡量标准,对不同的 EO数据集进行最先进的SR算法基准。我们还提议建立一个新型质量反馈网络(QMRNet),通过对图像的任何属性(即其分辨率、其扭曲性.)进行培训来预测质量(作为无引用度指标),并且能够优化用于特定度目标的超分辨率(GSD)图像属性(SR)的最新SR算法。这项工作是实施IQUAFLOW框架的一部分,该框架是评估图像质量质量、检测和分类以及EO的图像压缩案例。我们综合了我们的QMRNet算法,通过对图像的任何特性进行预测(如模糊性、锐度、鼻、中转和地面取样等图像的距离(GSQQQR)进行最精确的计算,并获得其准确的RRRRRRRRRR的准确比率,并用作以下的校准标定标定标标值,还。