StOMP-based compressed sensing image reconstruction system construction method
A technology of image reconstruction and compressed sensing, which is applied in 2D image generation, image coding, image data processing, etc., can solve the problems of reduced reconstruction accuracy, reduced stability, and partial signal loss, etc.
Pending Publication Date: 2021-09-07
ZHEJIANG UNIV OF TECH
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[0006] The present invention aims to solve the shortcomings of the traditional segmented orthogonal matching pursuit algorithm, such as partial signal loss, decreased reconstruction accuracy, and decreased stability, and proposes a method for constructing a StOMP-based compressed sensing image reconstruction system
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[0037] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.
[0038] In order to facilitate the public to understand the technical solution of the present invention, a brief introduction to the basic knowledge of compressed sensing is given below.
[0039] Compressed sensing theory shows that sampling the signal at a frequency much lower than that required by the Nyquis sampling theorem can also achieve accurate reconstruction of the signal. Its essence is that a small number of random linear projections of a compressible signal (that is, a signal with a sparse description on a certain basis) contain enough information to reconstruct and process the signal, that is, only use the prior knowledge of signal compressibility and Accurate reconstructions can be obtained with a small number of global linear measurements. This observation strategy can significantly reduce measurement time, sampling rate, and sa...
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The invention discloses an StOMP-based compressed sensing image reconstruction system construction method. The method comprises the following steps of: 1, acquiring an image; dividing the image into image blocks with the size of s*T, and both S and T being natural numbers; 2, converting image blocks of the image into a one-dimensional column vector X with the length of N, wherein N is equal to S*T; taking a partial Hadamard matrix phi=[phi1, phi2,... phim,... phiM] as a measurement matrix at a sampling end to sample X, obtaining an observation vector shown in the specification, Y being a column vector with the length of M, and M being a natural number; 3, performing mathematical modeling on the observation vector by using sparse representation to obtain a sparse coefficient of the observation vector in the signal; and 4, reconstructing an estimation signal at the reconstruction end sparse coefficient. The method is high in operation efficiency and high in accuracy, and the robustness of the system is enhanced.
Description
technical field [0001] The invention relates to a method for constructing a StOMP (Segmented Orthogonal Matching Pursuitalgorithm, StOMP) compressed sensing image reconstruction system. Background technique [0002] The traditional Nyquist sampling theorem requires that in the process of band-limited data sampling, the sampling rate must be greater than twice the highest frequency. On the one hand, due to the rapid increase in information demand, for example, in practical applications such as large-pixel and high-resolution image signals and sensor networks, as the signal bandwidth becomes larger and larger, the hardware devices for storing and transmitting data are also facing challenges. Facing great challenges; on the other hand, many practical signal processing processes first sample the signal with a high sampling frequency, then compress the data, and finally store or transmit it. During the compression process, only certain important data are retained, and a large am...
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Login to View More IPC IPC(8): G06T11/00G06T9/00G06F17/14G06F17/16
CPCG06T11/001G06T9/004G06F17/14G06F17/16
Inventor 郑建炜金倩倩
Owner ZHEJIANG UNIV OF TECH