Hyperspectral image compression method based on non local and low-rank decomposition

A hyperspectral image, low-rank decomposition technology, applied in the field of computer image processing, can solve the problems of loss of image spectral structure information, loss of spectral structure information, and destruction of single grayscale image spatial structure information, etc.

Active Publication Date: 2016-08-03
WUHAN UNIV
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Problems solved by technology

[0004] 2) Hyperspectral images are often treated as multiple grayscale images in previous applications, and each spectrum can be used as a grayscale image. This processing process will lose spectral structure information
[0005] 3) When some algorithms process hyperspectral images, they ...

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[0031] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0032] A hyperspectral image compression method based on non-local and low-rank decomposition provided by the present invention comprises the following steps:

[0033] Step 1: Crop the hyperspectral image whose length and width are not multiples of 8, so that the length and width are both multiples of 8, and the number of spectra remains unchanged;

[0034] Step 2: Standardize the cropped hyperspectral image so that each pixel value is between 0 and 255;

[0035] Step 3: Divide the standardized hyperspectral image into blocks, the space size of each block is...

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Abstract

The invention discloses a hyperspectral image compression method based on non local and low-rank decomposition. Non local similarity is used for partitioning the hyperspectral image, hyperspectral small blocks with the same size are acquired; and then, according to the similarity, similar blocks are gathered in one class, four-order data formed by blocks in the same class can be subjected to low-rank decomposition. As the size of the decomposed data is far smaller than that of the original data, compression of the hyperspectral data is thus realized, and the spatial structure characteristics and the spectral structure characteristics of the hyperspectral data are made full use of.

Description

technical field [0001] The invention belongs to the technical field of computer image processing, and relates to an image compression and reconstruction method, in particular to a hyperspectral image compression method based on non-local and low-rank decomposition. Background technique [0002] Hyperspectral images contain multiple bands. Compared with grayscale images and RGB images, hyperspectral images contain more spectral information, so the accuracy of image processing can be greatly improved. Although hyperspectral images are used more and more with the maturity of hyperspectral imaging technology and the reduction of cost, there are still some limitations in hyperspectral images. [0003] 1) Multiple spectra of hyperspectral images provide more useful information, but also provide more redundant information, which greatly increases the time complexity and space complexity of image processing. [0004] 2) Hyperspectral images are often treated as multiple grayscale i...

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Application Information

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IPC IPC(8): G06T9/00
CPCG06T9/00G06T2207/10036G06T2207/20081
Inventor 杜博章梦飞张乐飞张良培
Owner WUHAN UNIV
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