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

A measurement matrix and image reconstruction technology, which is applied in image enhancement, image analysis, image coding, etc., can solve the problem of low precision of RIP properties, achieve enhanced detail reconstruction capabilities, improve reconstruction accuracy, and facilitate algorithmic solutions

Inactive Publication Date: 2017-09-15
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 Block 0, 1 binary random measurement matrix, so that the sensor matrix can better meet the RIP conditions

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

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[0035] The present invention will be further described below in conjunction with the detailed description of the accompanying drawings.

[0036] Depend on figure 2 It can be seen from the schematic block diagram in the figure that the Figure three The constructed diagonal block binary random matrix is ​​used as the measurement matrix Φ, and the new measurement matrix reduces the condition number of the sensing matrix Θ(Θ=ΦΨ), so that the sensing matrix can better meet the RIP condition, which is more conducive to compression Perceptual reconstruction of images.

[0037] Use matlab to do image reconstruction simulation experiments on the grayscale images of Peppers, Lena and Fingerprint with a size of 512*512. In order to avoid the row and column effects formed after the image is reconstructed, the image rows and columns with a length of 512 are respectively carried out. Reconstruct, and finally average to obtain the reconstructed image. When the sampling rate is 0.25, the ...

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Abstract

The invention provides a construction optimization method for measurement matrix in image reconstruction based on compressed sensing. Linear measurement observation needs to be performed on images in image reconstruction based on compressed sensing, so original signals are sampled and compressed and signal dimensionality is greatly reduced. However, construction of the measurement matrix plays a vital role in linear measurement processes. The method comprises steps of based on a binary random measurement matrix, re-designing the measurement matrix; and constructing a very sparse opposite angle blocking measurement module. Thus, non-correlation of the matrix is greatly improved and the sensing matrix well meets the RIP condition. Through the new measurement matrix, hardware achievement is easy, reconstruction speed of the images is accelerated and image reconstruction precision is improved. When the sampling rate is in the region of 0.1-0.5, the peak signal to noise ratio (PSNR) of the reconstructed image is improved by 1-4dB.

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, the orthogonal sparse transformation will be performed to obtain s(k), which is denoted as x=Ψs, and the measur...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T9/00G06T5/00H03M7/30
CPCH03M7/3062G06T9/00G06T2207/20064G06T5/70
Inventor 魏子然徐智勇张健林
Owner INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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