Compressed sensing image reconstruction method based on relevance vector grouping

Abstract

The invention discloses a compressed sensing image reconstruction method based on relevance vector grouping, which mainly solves the problems of inaccuracy and low robustness of compressed sensing image reconstruction. The realization process is as follows: 1) receiving an observation matrix and an observation vector; 2) obtaining an initial relevance vector by the observation vector and a sending matrix; 3) dividing the relevance vector into sub-relevance vectors according to the spatial neighbourhood relationship of wavelet coefficients; 4) adding a component in each sub-relevance vector and sequencing the components; 5) updating the reconstructed wavelet high-frequency coefficients and observation vectors on the basis of a Bayesian framework according to the sequencing order; 6) carrying out invert wavelet transform on the reserved low-frequency wavelet decomposition coefficients and the reconstructed high-frequency wavelet coefficients to obtain a reconstructed image. Compared with OMP and BEPA methods, the compressed sensing image reconstruction method based on relevance vector grouping disclosed by the invention has the advantages of high quality and good robustness of the reconstructed image, and can be used for reconstruction for natural images and medical images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a compressed sensing image reconstruction method, which can be used to reconstruct medical images and 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 ...

AI Technical Summary

Problems solved by technology

[0005] In the document "Compressive sensing SAR image reconstruction based on Bayesian framework and evolutionary computation", Wu Jiao et al. used the wavelet domain to reconstruct high frequency, and obtained better reconstruction effect and faster reconstruction speed. However, its shortcoming is that in the reconstruction process, the correlation between the observation matrix and the observation is simply used as the reconstruction basis, and the statistical prior of the aggregation of the wavelet decomposition coefficients is not combined, which leads to the method not being robust, and the reconstruction The resulting image is not accurate enough

Images