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Structure optimization method for measurement matrix in image reconstruction based on compressed sensing

A technology of measurement matrix and image reconstruction, applied in image enhancement, image analysis, image coding and other directions, can solve the problem of low precision of RIP properties, achieve the effect of enhancing detail reconstruction ability, improving reconstruction accuracy, and facilitating algorithm solution

Inactive Publication Date: 2017-12-26
INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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Problems solved by technology

[0011] The technical problem to be solved by the present invention is: Aiming at the RIP property that the existing binary random measurement matrix cannot satisfy well in the compression sensing signal reconstruction and the problem that the accuracy of the reconstructed signal is not high, a very sparse diagonal Measure the matrix to make the sensing matrix better meet the RIP conditions

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  • Structure optimization method for measurement matrix in image reconstruction based on compressed sensing
  • Structure optimization method for measurement matrix in image reconstruction based on compressed sensing
  • Structure optimization method for measurement matrix in image reconstruction based on compressed sensing

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Embodiment Construction

[0038] The present invention will be further described below in conjunction with the detailed description of the accompanying drawings.

[0039] Such as figure 1 As shown, an optimization method for constructing a measurement matrix in image reconstruction based on compressed sensing, this method is based on a binary random measurement matrix, and constructs a very sparse diagonal measurement matrix, so that the improved measurement matrix and the sparse basis constitute The sensing matrix better satisfies the RIP conditions, which is more conducive to the reconstruction of sparse signals. Finally, the original signal is reconstructed from the sparse signal. The signal reconstruction process mainly includes the following steps:

[0040] Step 1. First measure the matrix according to figure 2 The diagonal block method shown is constructed. On the diagonal is a matrix of all 1s with a size of 1×(M / N). Except for the matrix on the diagonal, the remaining elements of the entire m...

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Abstract

The invention discloses a structure optimization method for a measurement matrix in image reconstruction based on compressed sensing. In compressed sensing image reconstruction, an image needs to be linearly measured and observed, original signals are sampled and compressed, signal dimension is greatly reduced, and the structure of the measurement matrix plays an important role in the linear measurement process. Based on a 0 and 1 binary random measurement matrix, the measurement matrix is redesigned, a super-sparsity diagonal measurement matrix is built, and irrelevance of the matrix is greatly improved, so that the sensing matrix more effectively meets RIP (routing information protocol) conditions. According to the new measurement matrix, hardware is easily implemented, calculation of the projection measurement process is simplified, and the PSNR (peak signal-to-noise ratio) and SSIM (structural similarity) of reconstructed images are greatly improved.

Description

technical field [0001] The invention relates to a construction optimization method of a measurement matrix in image reconstruction based on compressed sensing, which is characterized in that the original signal data of the signal is recovered and reconstructed at a lower sampling rate to reconstruct the original signal data with higher precision, which is applied to the compression and restoration of the signal , image processing and computer vision, etc., which belong to the field of signal compression transmission and restoration and reconstruction in signal and information processing. Background technique [0002] The core of compressive sensing is the linear measurement process. Let x(n) be the original signal, the length is N, and y(m) is obtained by multiplying the measurement matrix Φ by the left, and the length is M (M<N). If x(n) is not a sparse signal, an orthogonal sparse transformation will be performed to obtain s(k), which is denoted as x=Ψs, and the measure...

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Application Information

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IPC IPC(8): G06T5/00G06T5/10G06T9/00
CPCG06T5/10G06T9/00G06T2207/20064G06T5/70
Inventor 魏子然徐智勇张健林
Owner INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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