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Spatial and Spectral Coordinate Super Resolution of Hyperspectral Imagery based on Redundant Dictionary

机译:基于冗余字典的高光谱影像空间光谱坐标超分辨率

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Hyperspectral imagery has been widely used in various fields for its rich amount of feature information. The quality of hyperspectral imagery has been set higher requirements. As a result of the limitation of imaging semiconductor technology, hyperspectral image resolution needs to be improved by a signal processing method. This paper presents a recovery algorithm of spatial and spectral coordinate super-resolution of hyperspectral image based on redundant dictionary. Compared with the traditional image super-resolution restoration algorithm, the super-resolution restoration in the spectral of hyperspectral image was added on the basis of spatial resolution improvement. The original constraint was added in the algorithm and edges of reconstructed image were sharpened with the Maximum a Posterior. The results show this algorithm can effectively improve spatial and spectral resolution of the hyperspectral imagery.
机译:高光谱图像因其丰富的特征信息而被广泛用于各个领域。高光谱图像的质量已被设定了更高的要求。由于成像半导体技术的局限性,需要通过信号处理方法来提高高光谱图像的分辨率。提出了一种基于冗余字典的高光谱图像空间和光谱坐标超分辨率恢复算法。与传统的图像超分辨率恢复算法相比,在空间分辨率提高的基础上,增加了高光谱图像光谱中的超分辨率恢复。在算法中添加了原始约束,并使用“最大后验”来锐化重建图像的边缘。结果表明,该算法可以有效提高高光谱图像的空间和光谱分辨率。

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