Rapid reconstructing method of compressed sensing image based on least square optimization

Abstract

The invention discloses a rapid reconstructing method of a compressed sensing image based on least square optimization. Optimized reconstruction is carried out to signals basing on a least square method; a parameter-whole correlation degree is measured by newly defined whole correlation; a most correlated atom aiming at an image is selected; the number of iterations is reduced; a subblock reconstruction theory is introduced; a subblock size and a measuring matrix are redesigned; the calculation scale of the reconstruction operation is reduced; the optimized reconstruction is carried out to signals basing on the least square method; the reconstructing precision and the convergence rate are ensured; the experiment result shows that the reconstructing time of the signals is remarkably reduced through the FBWRFI algorithm; the increasing trend of the reconstructing time increasing at high speed as the signals increase is changed into linearity; and the effectiveness of the algorithm is proved.

Description

technical field [0001] The invention relates to a compression sensing image reconstruction method, in particular to an image-oriented compression sensing sampling fast reconstruction method based on least square optimization. Background technique [0002] Compressed sensing or compressive sampling (CS) based on sparse representation theory and functional analysis-approximation theory is a new sampling method, which can break through the limitation of the famous Shannon-Nyquist sampling theorem when sampling compressible signals. The signal is sampled at a sampling rate twice the signal's maximum bandwidth, reducing the amount of data acquired. With its advantage of representing the full-length signal with a small number of sampling values, compressed sensing has attracted research enthusiasm in many fields such as signal, communication, electronic information, statistical theory, codec theory, and computer at the beginning of its appearance. The most significant research ac...

AI Technical Summary

Problems solved by technology

[0022] from figure 1 It can be seen from Table 3 that although the parallelization and acceleration of the compressed sensing algorithm can significantly reduce the execution time of the algorithm, it cannot change the trend that the algorithm grows at a very high rate as the signal increases

The reconstruction time of the compressed sensing algorithm is increasing rapidly, and the growth trend is expanding, making the execution time of the reconstruction algorithm unpredictable, which is not conducive to the automatic resource allocation when the cloud platform accelerates

Images