An Image Signal Acquisition Method Based on Compressed Sensing

Abstract

The invention discloses a compressed sensing-based image signal acquisition method. The method comprises the steps of receiving an analog image signal x(t), sampling the analog image signal x(t), andacquiring a sampled discrete signal yi(n) of an i path; according to the analog image signal x(t) and the discrete signal yi(n), acquiring a fourier transform function X(Omega) of x(t), a discrete fourier transform function Yi(Omega) of yi(n), an ideal sensing matrix H(Omega) and an original signal R(Omega); calculating a unique sparse solution of the original signal R(Omega) according to the ideal sensing matrix H(Omega); and reconstructing the sampled analog image signal and the discrete signal according to the unique sparse solution of the original signal R(Omega). Through adoption of the method, the sparse signal can be obtained efficiently, each signal channel is matched, and zero overflow is realized; sampling and reconstruction errors of the sparse signal in the prior art are overcome and are corrected accurately; and compressed sensing prevents impact on the whole system.

Description

technical field [0001] The invention relates to the field of sparse signal acquisition, in particular to an image signal acquisition method based on compressed sensing. Background technique [0002] Most of the existing sampling methods for analog signals use the shanoon theorem. Most signals suitable for this sampling mode occupy almost the entire bandwidth. In fact, with the development of information technology, many signals often have sparse characteristics. For example, image signals, radar signals, etc., that is, signals that only occupy a limited frequency band in their entire bandwidth at a certain moment, if Shanoon's theorem is still used, the sampling efficiency will be low. [0003] In view of this, someone proposed the compressed sensing theory to solve the sampling problem of sparse signals; however, the errors caused by the gain, phase mismatch and time jitter caused by the channel mismatch of the system will have an impact on the entire system , therefore, t...

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Problems solved by technology

[0003] In view of this, someone proposed the compressed sensing theory to solve the sampling problem of sparse signals; however, the errors caused by the gain, phase mismatch and time jitter caused by the channel mismatch of the system will have an impact on the entire system , therefore, there are few applications of compressive sensing in practical circuits

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