激光与光电子学进展, 2019, 56 (4): 041001, 网络出版: 2019-07-31
高光谱超分图像质量评价 下载: 1134次
Quality Assessment of Hyperspectral Super-Resolution Images
图像处理 高光谱图像 超分重建 无参考测度 图像质量评价 image processing hyperspectral images super-resolution reconstruction no-reference metric image quality assessment
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.
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. Song Xue, Siyu Zhang, Yongfeng Liu. Quality Assessment of Hyperspectral Super-Resolution Images[J]. Laser & Optoelectronics Progress, 2019, 56(4): 041001.