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Genetic evolution image rebuilding method based on Ridgelet redundant dictionary

A redundant dictionary and image reconstruction technology, which is applied in image enhancement, image data processing, genetic models, etc., can solve the problems of inaccurate reconstruction results, poor robustness, and no theoretical support, etc. Structural efficiency, weakening block effect and noise interference, and reducing time complexity

Active Publication Date: 2012-07-25
XIDIAN UNIV
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Problems solved by technology

[0010] 2) In practical applications, the orthogonal matching pursuit algorithm reconstructs the signal at a given number of iterations, that is, under the condition of satisfying a certain sparsity constraint, and the sparsity value is artificially set without theoretical support. The method of forcing the iterative process to stop makes the reconstruction result not very accurate and the robustness is not good

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  • Genetic evolution image rebuilding method based on Ridgelet redundant dictionary
  • Genetic evolution image rebuilding method based on Ridgelet redundant dictionary
  • Genetic evolution image rebuilding method based on Ridgelet redundant dictionary

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Embodiment 1

[0077] The present invention is a genetic evolutionary image reconstruction method based on Ridgelet redundant dictionary. Signal reconstruction is a means to restore incomplete signals, the last link of compressed sensing (CS), and the core of compressed sensing theory. And key, image reconstruction is also a kind of signal reconstruction. In order to realize image reconstruction in the present invention, what needs to be found is a redundant base dictionary with good sparse representation performance and a solution for L 0 An effective method for the NP-hard problem of norm combination optimization, thus the present invention establishes a genetic evolution compressed sensing reconstruction method based on Ridgelet redundant dictionary. The first part of this method is to use the information of similar image blocks in the image, select the affine propagation AP algorithm that is insensitive to the cluster center and the number of categories to cluster and group all the image...

Embodiment 2

[0124] The genetic evolution image reconstruction method based on Ridgelet redundant dictionary is the same as embodiment 1, combined figure 1 with figure 2 , the specific implementation process of the present invention is described in detail as follows:

[0125] Step 1: Cluster the observation vectors

[0126] For the observation vectors of all image blocks sent by the image sender, the affine propagation clustering AP algorithm is used to group similar observation vectors together to obtain observation vectors of multiple categories.

[0127] In this embodiment, the 512×512 image is divided into 16×16 image blocks to obtain 1024 image blocks; all image blocks are saved as column vectors, and the column vectors corresponding to all image blocks are multiplied by the Gaussian observation matrix to obtain 1024 observation vector.

[0128] The specific steps of the affine propagation clustering AP algorithm are:

[0129] The first step is to set the moment when the number o...

Embodiment 3

[0198] The genetic evolution image reconstruction method based on Ridgelet redundant dictionary is the same as embodiment 1-2,

[0199] The present invention and the reconstruction effect comparison simulation experiment of existing OMP algorithm:

[0200] The purpose of the experiment is to solve the L 0 The present invention and the existing OMP algorithm compress the sensing reconstruction effect of the norm problem. The experimental test data are two standard test images Barbara and Lena with a size of 512×512, both of which adopt the idea of ​​​​blocking for Gaussian observations, wherein the initial value of the image block sparsity of the present invention is set to 32, self-adaptive adjustment during evolution, evolution algebra is 5, while the image block sparsity in the OMP algorithm is fixed at 32. Table 2 lists the comparison of PSNR value of reconstructed images by two compressed sensing reconstruction methods. The Barbara diagram is based on the data comparison...

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Abstract

The invention discloses a genetic evolution image rebuilding method based on a Ridgelet redundant dictionary, and the method is used for solving the problem that the image rebuilt by the existing L0 norm rebuilding technology is poor in visual effect. A rebuilding process comprises the following steps of: clustering all partitioning observation vectors according to the level of similarity by selecting a proper clustering algorithm; initiating clusters; carrying out the common genetic evolution on the initiated clusters; rebuilding an initial image; updating by means of filtering and convex projecting; judging whether evolution algebra reaches a maximum value or not; updating the sparsity; updating the clusters; carrying out the independent genetic evolution on the image blocks; and rebuilding the image. In the method, the similar clusters of the image blocks are used, and the optimal Ridgelet redundant dictionary base atom is found for each image block of each cluster by a genetic evolution computation thought, so that the time complexity of the algorithm is reduced, the blocking effect in the rebuilt image is removed by means of filtering and convex projecting, the search space of the optimal solution is shortened, the image is high in rebuilding precision, and the image is good in rebuilding effect, so that the method can be used for the fields of image processing and computer vision.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to the method of introducing evolutionary calculation to solve the non-convex optimization reconstruction technology of natural images under the framework of compressed sensing, specifically a genetic evolutionary image reconstruction method based on Ridgelet redundant dictionary, which can be used for image processing and computer vision fields. Background technique [0002] In compressed sensing theory, signal reconstruction is a means to restore the incomplete signal, it is the last link of compressed sensing (CS), and it is also the core and key of compressed sensing theory. According to the theory of compressed sensing, the process of signal reconstruction can be transformed into the problem of solving the underdetermined equations. On the surface, it is impossible to solve the unique definite solution of the underdetermined equations, but E.Candes et al. proved that in ...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T5/50G06N3/12
Inventor 刘芳焦李成郝红侠杨丽戚玉涛周确侯彪王爽杨淑媛马文萍尚荣华
Owner XIDIAN UNIV
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