Non local joint sparse representation based hyperspectral image super-resolution reconstruction method

Abstract

The invention relates to a non local joint sparse representation based hyperspectral image super-resolution reconstruction method. The method comprises: firstly, performing dictionary training on a low-spatial-resolution hyperspectral image with an online dictionary training method to obtain a corresponding spectral dictionary; secondly, performing joint sparse representation on similar pixel vectors by virtue of a full-color image of a same scene, and reconstructing a high-resolution image; and finally, processing the reconstructed high-resolution image by utilizing an iterative reverse projection technology to obtain a high-resolution hyperspectral image with smaller reconstruction error and higher visual quality. According to the method, the similar pixel vectors are subjected to non local joint sparse representation by utilizing the non local self-similarity property of the image, so that the visual quality of the reconstructed image is improved and structural features of edges, textures and the like of the image are reconstructed more effectively in an empty region while image spectral information is kept complete; and multiple wavebands of the hyperspectral image are subjected to sparse representation and reconstruction at the same time, so that the hyperspectral image with relatively high definition and identification degree can be reconstructed.

Description

technical field [0001] The invention belongs to the field of hyperspectral image processing, and in particular relates to a hyperspectral image super-resolution reconstruction method based on spectral dictionary training and non-local joint sparse representation. Background technique [0002] The multispectral nature of hyperspectral image (Hyper-Spectral Image, HSI) makes it have important applications in many fields such as military monitoring, remote sensing and medical diagnosis. However, due to the special imaging mechanism, the hyperspectral image loses spatial information while improving the spectral resolution, resulting in a decrease in its spatial resolution. In order to obtain hyperspectral images with both high spatial resolution and high spectral resolution, the super-resolution reconstruction method of HSI is proposed. [0003] Among the many methods to improve the spatial resolution of HSI, the panchromatic sharpening method aims to utilize the fusion of panc...

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Problems solved by technology

[0006] In order to avoid the shortcomings of the existing technology, the present invention proposes a hyperspectral image super-resolution reconstruction method based on non-local joint sparse representation, which overcomes the limitations of the existing hyperspectral image super-resolution reconstruction method on spatial domain edges, textures, etc. Structural features are incompletely preserved, spatial detail information is not rich enough, etc.

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