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Compressed sensing image reconstruction method

A sensor map and reconstructed image technology, applied in the field of image processing, can solve the problems of low reconstructed image quality and slow convergence speed, so as to improve subjective visual effect and peak signal-to-noise ratio, improve performance, and speed up convergence speed Effect

Inactive Publication Date: 2016-12-07
JIANGSU UNIV
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Problems solved by technology

[0008] The purpose of the present invention is to: aim at the shortcomings of the existing image compression sensing reconstruction algorithm that the convergence speed is slow, the quality of the reconstructed image is not high, and the neighborhood statistical characteristics of the sparse representation coefficient are not used, and the observation value is obtained by random projection , and then use the observed values ​​to reconstruct

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Embodiment Construction

[0026] The image reconstruction method will be further described below mainly in conjunction with the accompanying drawings and specific embodiments.

[0027] This embodiment selects the standard gray image Barbara whose original image size is 512 * 512 pixels to analyze, and illustrates the corresponding results after the implementation of the present invention. The specific steps are as follows:

[0028] A. Initialize the reconstructed image;

[0029] B. Perform random projection sampling on the initialized image x, and calculate the observed value;

[0030] C. Before the wavelet domain, a non-parametric orthogonal polynomial density model is used to denoise the image for the previous estimated value;

[0031] D. Using the iterative non-parametric orthogonal polynomial density model of the observed value under the orthogonal wavelet decomposition to realize reconstruction;

[0032] E. Repeat iterations to obtain new estimated values ​​until the reconstruction is completed;...

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Abstract

The invention relates to a compressed sensing image reconstruction method and belongs to the field of image processing. According to the method, an original image is reconstructed from a random projection observation value much lower than a Nyquist sampling rate based on priori knowledge of image sparse representation, and the performance of an algorithm is further improved in combination with total variation regulation. The theory and practice prove that the method has the advantages that the shortcomings of low convergence speed and no use of neighborhood statistics properties of a transformation coefficient in a conventional compressed sensing algorithm are properly overcome, the subjective visual effect of the reconstructed image can be effectively improved, the peak signal to noise ratio of the reconstructed image can be effectively increased, and the convergence speed of the compressed sensing image reconstruction algorithm is increased.

Description

technical field [0001] The invention relates to a compression sensing image reconstruction method, which belongs to the field of image processing. Background technique [0002] In the field of image processing, reconstruction for compressive sensing is one of the most important ill-conditioned inverse problems under sparse representation. Compressive sensing image reconstruction exploits the prior knowledge that an image can be sparsely represented to reconstruct the original image from randomly projected observations at a much lower sampling rate than Nyquist. [0003] In traditional signal or image acquisition systems, Shannon sampling theorem is the basic principle that must be followed: that is, the signal sampling frequency must be greater than or equal to the Nyquist sampling rate (twice the signal bandwidth) to reconstruct the original signal without distortion. In order to meet the bandwidth constraints of Shannon sampling theorem, the current sampling system will r...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T9/00
Inventor 宋余庆刘哲汤峥
Owner JIANGSU UNIV
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