Hyperspectral image super-resolution algorithm based on non-negative structure sparse

Abstract

The invention discloses a hyperspectral image super-resolution reconstruction algorithm based on matrix structure sparse non-negative decomposition. According to the reconstruction algorithm, a low-spatial-resolution hyperspectral image and a high-resolution color image are united to reconstruct a high-resolution hyperspectral image, and the problem that an existing algorithm can not accurately restore the high-resolution hyperspectral image. The method comprises the realizing steps that (1) the low-resolution hyperspectral image and the corresponding high-resolution color image are input; (2) local and non-local self-similarity of the hyperspectral images is utilized for constructing a spectrum reconstruction target function based on the matrix structure sparse non-negative decomposition; (3) an alternating direction multiplier method is adopted for alternative solving to obtain an optimized spectrum material coefficient and a spectrum material base; (4) a matrix of the optimized spectrum material coefficient and a matrix of the optimized spectrum material base are utilized to reconstruct the high-resolution hyperspectral image. According to the method, the restored hyperspectral image is clearer, the image edge is sharper, and the spatial resolution of the hyperspectral image can be effectively increased.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a high-resolution hyperspectral image reconstruction method, which is mainly used for joint reconstruction of a high-resolution hyperspectral image from a low-resolution hyperspectral image and a high-resolution color image. Background technique [0002] Hyperspectral images have important demands in national defense scientific research and military applications. Due to the inherent imaging characteristics of traditional high spatial resolution imaging and the limitations of high-resolution sensors, it is difficult for existing hyperspectral imaging methods to achieve high resolution in hyperspectral imaging. Therefore, using signal processing technology to reconstruct high-resolution hyperspectral images from low-resolution hyperspectral images has become an important way to obtain high-resolution hyperspectral images. [0003] In order to obtain high-resolution hypers...

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the existing spectral super-resolution reconstruction algorithm based on matrix non-negative decomposition does not use the similarity between local and non-local spectral images, and propose a spectral image super-resolution reconstruction based on matrix structure sparse non-negative decomposition Algorithm to reconstruct high-precision high-resolution spectral images from low-resolution spectral images and high-resolution color images

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