Edge constraint based image reconstruction method under multivariate observation
An image reconstruction and edge-constrained technology, applied in the field of image processing, can solve problems such as inaccurate images, lack of robustness, and ignoring the guiding role of wavelet low-frequency coefficients
Active Publication Date: 2015-06-10
XIDIAN UNIV
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This method constructs a multivariate distribution model for wavelet coefficients, grasps the characteristic of aggregation of wavelet coefficients, and models its statistical correlation, but this method ignores the guiding role of retained wavelet low-frequency coefficients for image reconstruction. As a result, it is not robust and the reconstructed image is not accurate enough
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[0042] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0043] refer to figure 1 , the specific implementation steps of the present invention are as follows:
[0044] Step 1, the sender sends the observation matrix, low-frequency wavelet decomposition coefficients and high-frequency multivariate measurement matrix.
[0045] (1a) The image sender observes the image in the wavelet domain, and reserves all the low-frequency wavelet decomposition coefficients as observations of the low-frequency wavelet decomposition coefficients, and uses the orthogonal random Gaussian observation matrix Φ to measure the horizontal high-frequency sub-band coefficient A 1 , Vertical high-frequency sub-band coefficient A 2 and the diagonal high-frequency subband coefficients A 3 Multivariate observations are carried out separately to obtain the horizontal high-frequency sub-band multivariate measurement matrix Y 1 ...
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The invention discloses an edge constraint based image reconstruction method under multivariate observation and mainly aims to solve the problems of the prior art of compressed sensing image reconstruction inaccuracy and low robustness. The edge constraint based image reconstruction method includes: 1) receiving an observation matrix, a low frequency wavelet decomposition coefficient and a multivariate measurement matrix; 2) acquiring a nonzero coefficient group supporting set through edge detection and relevant guides; 3) reconstructing high frequency wavelet coefficient in the nonzero coefficient group supporting set on the basis of a multivariate Gaussian model according to the observation matrix, the multivariate measurement matrix, basic covariance and residual covariance matrix in the Gibbs sampling method; 4) converting the low frequency wavelet decomposition coefficient and the reconstructed high frequency wavelet coefficient to obtain reconstruction images. Compared with OMP and BEPA method, the edge constraint based image reconstruction method has the advantages of high reconstruction image quality and good robustness, and can be reconstruction of natural images and medical images.
Description
technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a statistical compressed sensing image reconstruction method, which can be used to reconstruct natural images. Background technique [0002] In recent years, a new data theory compressive sensing CS has emerged in the field of signal processing. This theory realizes compression while collecting data, breaks through the limitations of the traditional Nyquist sampling theorem, and brings new advantages to data collection technology. The revolutionary changes make the theory have broad application prospects in compressed imaging systems, military cryptography, wireless sensing and other fields. Compressed sensing theory mainly includes three aspects: sparse representation of signal, observation of signal and reconstruction of signal. Among them, designing a fast and effective reconstruction algorithm is an important part of successfully promoting and applying C...
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Inventor 刘芳李婉李玲玲郝红侠焦李成杨淑媛尚荣华张向荣马文萍
Owner XIDIAN UNIV