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高光谱超分图像质量评价

Quality Assessment of Hyperspectral Super-Resolution Images

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摘要

利用经典超分方法获得高光谱超分图像集,研究了图像的特点,提出一种基于3类图像特征向量的高光谱超分图像质量评价方法。该方法分别计算了图像的空域自然场景统计、局部频域特征以及局部二值模式梯度,获得了3类特征向量,对3类低层特征向量建立回归森林模型,以预测图像的质量得分。与其他经典方法相比,所提算法具有更好的准确度和主客观一致性。

Abstract

The hyperspectral super-resolution image set is obtained with the classical super-resolution method and the characteristics of these images are studied. A quality assessment method of hyperspectral super-resolution images is proposed based on three types of image feature vectors. In this method, the spatial natural statistics, the local frequency features and the local binary gradient of images are calculated, respectively, and three kinds of feature vectors are obtained. The regression forest model is established for the three types of low-level feature vectors to predict the image quality scores. Compared with other classical methods, the proposed algorithm possesses high accuracy and good subjective and objective consistency.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391.41

DOI:10.3788/lop56.041001

所属栏目:图像处理

基金项目:国家自然科学基金(61379105)

收稿日期:2018-08-23

修改稿日期:2018-08-27

网络出版日期:2018-08-31

作者单位    点击查看

薛松:陆军炮兵防空兵学院兵器工程系, 安徽 合肥, 230000
张思雨:陆军炮兵防空兵学院研究生1队, 安徽 合肥, 230000
刘永峰:陆军炮兵防空兵学院兵器工程系, 安徽 合肥, 230000

联系人作者:刘永峰(954271756@qq.com); 张思雨(yusonzhang@foxmail.com);

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引用该论文

Xue Song,Zhang Siyu,Liu Yongfeng. Quality Assessment of Hyperspectral Super-Resolution Images[J]. Laser & Optoelectronics Progress, 2019, 56(4): 041001

薛松,张思雨,刘永峰. 高光谱超分图像质量评价[J]. 激光与光电子学进展, 2019, 56(4): 041001

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