Infrared and Laser Engineering, Volume. 47, Issue 10, 1026002(2018)
Hyperspectral image denoising and antialiasing based on tensor space and reciprocal cell
Conventtrial denoising and antialiasing algorithms are usually for single band images. Previously, numerous studies have only designed for single band images. Aiming at the data characteristics of hyperspectral image and the influence of noise and aliasing on the image, a multidimensional filtering algorithm combining tensor and reciprocating cells was proposed and applied to denoising and antialiasing of hyperspectral images. The method introduced the tensor, and the hyperspectral image data was regarded as the third?蛳order tensor expression. The reciprocal cell was used to obtain the spectrum extrapolation which containd less image aliasing and noise. From the point of view of the minimum mean square error, the algorithm alternately iterated to solve the three directions of the filter, and finally completed the image filtering. The algorithm could effectively reduce the image aliasing and noise under the premise of ensuring the consistency of image space and spectral information. The effectiveness of the proposed algorithm was proved by comparing with multiple sets of hyperspectral data of the two?蛳dimensional Wiener filter algorithm and tensor multidimensional denoising algorithm.
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Zhang Aiwu, Zhao Jianghua, Zhao Ningning, Kang Xiaoyan, Guo Chaofan. Hyperspectral image denoising and antialiasing based on tensor space and reciprocal cell[J]. Infrared and Laser Engineering, 2018, 47(10): 1026002
Category: 信息获取与辨识
Received: May. 7, 2018
Accepted: Jun. 12, 2018
Published Online: Nov. 25, 2018
The Author Email: Aiwu Zhang (zhangaw98@163.com)