Image Reconstruction Method Based on Genetic Sparse Optimization
An image reconstruction and sparse optimization technology, applied in the field of image processing, can solve the problems of poor reconstruction results and damage to the structure of the PCA dictionary, and achieve the effects of good area and boundary consistency, clear visual effects, and small artificial block effects
Active Publication Date: 2017-02-15
XIDIAN UNIV
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However, this method faces an important problem in practical application: when the number of samples is insufficient, this update will destroy the structure of the PCA dictionary and make the reconstruction result worse
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[0031] refer to figure 1 , the specific implementation steps of the present invention are as follows:
[0032] Step 1, input the observation vector y of the image block x, and estimate the DC component of the image block and variable components
[0033] 1.1) The input size is Pixel image block x, get observation vector y:
[0034] y=Φx
[0035] Among them, Φ is an M×N observation matrix, M is the observation dimension, and N is the signal dimension;
[0036] 1.2) According to the observation vector y and the observation matrix Φ, estimate the DC component
[0037]
[0038] Among them, 1 is an N-dimensional vector whose elements are all 1, and the superscript Represents the Moore-Penrose pseudo-inverse of the matrix;
[0039] 1.3) According to the observation vector y, observation matrix Φ and DC component Estimated Variation Component
[0040]
[0041] Step 2, smooth and non-smooth classification of the image block x.
[0042] 2.1) Calculate the thres...
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The invention discloses an image reconstruction method based on heredity sparse optimization and a Bayes estimation model. The method mainly solves the problems that in the partitioning and reconstruction process of a compressed sensing image through the existing method, a boundary is fuzzy and a blocking effect is obvious. According to the method, image blocks are classified into smooth image blocks and non-smooth image blocks, and modeling and reconstruction are respectively carried out on the smooth image blocks and the non-smooth image blocks; according to the statistical property of the smooth image blocks, rapid reconstruction is carried out on the direct current components and the variable components of the smooth image blocks by directly using pseudo-inverse solutions; for the non-smooth image blocks, optimal reconstruction is carried out on the non-smooth image blocks by selecting a set of atoms from a PCA dictionary through a genetic algorithm. Experimental results show that compared with images reconstructed through a traditional orthogonal matching pursuit method (OMP) and a traditional statistical compression sensing method (SCS), the images reconstructed through the method has better boundary consistency and area consistency, detail information is clearer, the blocking effect is reduced remarkably, and the image reconstruction method based on heredity sparse optimization and the Bayes estimation model can be applied to reconstruction of images acquired under a low sampling rate.
Description
technical field [0001] The invention belongs to the technical field of image processing, in particular to an image reconstruction method, which can be used to solve the problem of block compression sense reconstruction of natural images. Background technique [0002] In the development of image processing, the proposal of compressed sensing has made great progress in image acquisition technology. It can reconstruct the original image only through a small number of observations, which greatly improves the efficiency of image acquisition. This is of great significance to national construction and military development. [0003] In recent years, algorithms to solve compressed sensing image reconstruction emerge in an endless stream, the most commonly used is the iterative method based on greedy pursuit technology. The basic idea is to make the observation error converge quickly by selecting the atom with the greatest correlation with the residual in each iteration. Representat...
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IPC IPC(8): G06T5/00
Inventor 焦李成张思博李玲玲杨淑媛郝红侠尚荣华马文萍马晶晶
Owner XIDIAN UNIV