Method for reconstructing compressively sensed spectral image based on structural clustering sparse representation

A spectral image and sparse representation technology, applied in the field of image processing, can solve the problems of difficult high-quality spectral images and the lack of correlation information of sparse coefficients

Active Publication Date: 2014-05-21
XIDIAN UNIV
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Problems solved by technology

However, the existing spectral reconstruction technology is mainly constrained by the sparsity of the spectral segment image, and does not ut

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  • Method for reconstructing compressively sensed spectral image based on structural clustering sparse representation
  • Method for reconstructing compressively sensed spectral image based on structural clustering sparse representation
  • Method for reconstructing compressively sensed spectral image based on structural clustering sparse representation

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Embodiment Construction

[0040] refer to figure 1 , the present invention is a compressive sensing spectral image reconstruction method based on structural clustering sparse representation, and its implementation steps are as follows:

[0041] Step 1: Input observation data.

[0042] Input spectral image observation result y∈R h(w+n-1)m and observation matrix H∈R h(w+n-1)m×hwn , where h and w represent the height and width of each spectral segment image respectively, n represents the number of spectra, m represents the number of observations, R represents the real number domain, x represents the original spectral image to be solved, x∈R hwn .

[0043] Step 2: Set back projection coefficient δ, sparse coefficient threshold t 1 , maximum iteration step number P, update step size L, spectral image block category number K, image block similarity threshold τ and similarity weight parameter h 0 . The initial recovered spectral image is x (0) =H T y, x (0) ∈ R hwn , set the current iteration number...

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Abstract

The invention discloses a method for reconstructing a compressively sensed spectral image based on structural clustering sparse representation, which method solves the difficulty of the existing method for reconstructing the spectral image that a partial structure of the spectral image is hard to be recovered accurately since spatial and spectral correlations are not fully used. The method comprises implementation steps as follows: 1, performing back projection upon input encoded sensing data of an spectral image to obtain an initially reconstructed spectral image; 2, segmenting the reconstructed spectral image to obtain a series of overlapped three-dimensional spectral image blocks; 3, de-noising the three-dimensional spectral image blocks by using a sparse representation method based on structural clustering; 4, recovering the whole spectral image by using the spectral image blocks subjected to de-noising procession; 5, updating the spectral image by using a back projection technology, iterating the steps 2-5 so as to obtain a final reconstruction result. Experiment results show that the method disclosed by the invention can reconstruct a relatively fine spectral image structure, and the reconstructed spectral image has a relatively high signal to noise ratio.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a reconstruction method for compressed sensing spectral images, which is mainly used for high-quality restoration of spectral images. Background technique [0002] The imaging characteristics of the combination of spectral image and spectral map make it widely used in scientific research and military fields such as vegetation biological monitoring, land dynamic monitoring, mineral distribution investigation, target detection, and offshore environmental monitoring. In order to obtain high-resolution spectral images, many scholars have done a lot of research work on improving the performance of optical devices and developing high-performance image sensors. However, the achieved results are still difficult to meet people's needs for high-resolution spectral images. The main reason is that the internal imaging mechanism of the traditional spectral imaging system is difficult...

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Application Information

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IPC IPC(8): G06T17/00
Inventor 董伟生马碧玉牛毅石光明高大化刘丹华
Owner XIDIAN UNIV
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