Compressed sensing image reconstructing method based on prior model and 10 norms

A technology of compressed sensing and prior models, applied in the field of image processing, can solve the problems of large number of observations, large amount of calculation, and low image quality, and achieve the effects of expanding application range, improving reconstruction quality, and reducing computational complexity

Inactive Publication Date: 2011-08-10
XIDIAN UNIV
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Problems solved by technology

These two types of algorithms have their inherent shortcomings. The convex relaxation method uses a small number of observations to reconstruct the signal, and the quality of the reconstructed image is high, but it has a large amount of calculation and high time cost; the greedy pursuit algorithm and convex relaxation Compared with the traditional me

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  • Compressed sensing image reconstructing method based on prior model and 10 norms
  • Compressed sensing image reconstructing method based on prior model and 10 norms
  • Compressed sensing image reconstructing method based on prior model and 10 norms

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[0054] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0055] Step 1, using wavelet low-frequency sub-bands to obtain three position populations corresponding to three high-frequency sub-bands.

[0056] refer to figure 2 , the specific implementation of this step is as follows:

[0057] (1a) Perform inverse transformation on the one-scale wavelet low-frequency sub-band sent by the sender, and set all high-frequency coefficients to zero to obtain an image with blurred edges;

[0058] (1b) Perform canny edge detection on the blurred image to obtain an image containing edge information;

[0059] (1c) Perform wavelet transform on the image containing edge information to obtain three high-frequency sub-bands containing edge information, and determine the modulus value of the coefficient in each high-frequency sub-band according to the statistical distribution of the modulus of the coefficients. The position of zero is marked as ...

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Abstract

The invention discloses a compressed sensing image reconstructing method based on a prior model and 10 norms, mainly used for solving the defects of poor visual effect and long operation time existing in image reconstruction in the prior art. In the technical scheme of the invention, a compressed sensing image reconstruction frame with 10 norms is optimized by utilizing a prior model; and the positioning of sparsity coefficient and solution of the sparsity coefficient value are achieved through two effective steps: step 1, establishing the prior model, and carrying out low frequency coefficient inverse wavelet transform so as to obtain an image with a fuzzy edge, determining the position of the edge by edge detection, and searching the position of wavelet high frequency subband sparsity coefficient through an immunization genetic algorithm by using the prior model of which the wavelet coefficient has inter-scale aggregation; and step 2, solving a corresponding high frequency subband by using an improved clone selective algorithm, and then carrying out the inverse wavelet transform so as to obtain a reconstructed image. Compared with the prior art, the method has the advantages of good visual effect and low calculation complexity, and can be used in the fields of image processing and computer visual.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to a compression sensing image reconstruction method, which can be used in practical engineering fields such as biological sensing, remote sensing image processing, wireless sensor network and image acquisition equipment development. Background technique [0002] With the advent of digitization and information age, people pay more and more attention to digital image processing technology, especially in the fields of SAR image processing, medical image processing and remote sensing image processing. In recent years, Donoho et al. proposed a novel theory - compressive sensing theory CS. In this theory, the sampling speed is no longer determined by the bandwidth of the signal, but by the structure and content in the signal. Based on the theory of compressed sensing, the signal can be sampled at low speed and then encoded, which greatly reduces the computational complexity. Once...

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Application Information

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IPC IPC(8): H04N7/26G06T9/00H04N19/146H04N19/42
Inventor 刘芳焦李成王爽孙菊珍郝红侠侯彪戚玉涛郜国栋马文萍尚荣华杨淑媛
Owner XIDIAN UNIV
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