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Progressive compressed sensing reconstruction method and system based on Hadamard matrix

A compressed sensing, progressive technology, applied in instruments, graphics and image conversion, computing, etc., can solve the problems of controlling the sampling rate, high-resolution image time, inability to monitor the quality of image acquisition in real time, etc., and achieve good image compression effect. , the effect of reducing the sampling rate

Active Publication Date: 2020-10-16
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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Problems solved by technology

[0013] In order to solve the above-mentioned technical problems, the purpose of the present invention is to solve the problem that compressed sensing takes a long time to collect high-resolution images, cannot monitor the quality of image collection in real time, and cannot better control the sampling rate at the same time. A method based on Hadamard matrix characteristics is proposed. A Reconstruction Method for Progressive Compressed Sensing Imaging

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  • Progressive compressed sensing reconstruction method and system based on Hadamard matrix
  • Progressive compressed sensing reconstruction method and system based on Hadamard matrix
  • Progressive compressed sensing reconstruction method and system based on Hadamard matrix

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[0043] The invention discloses a progressive compressed sensing reconstruction method based on Hadamard matrix, which includes:

[0044]Step 1. Obtain the original Hadamard matrix and the original image, use the original Hadamard matrix as a low-order Hadamard matrix, convert the row data in the low-order Hadamard matrix into the first observation matrix, and use the first observation matrix Perform compressed sensing sampling on the original image, obtain the low-level measurement value of the original image, and output the reconstructed image generated according to the low-level measurement value to the user for viewing in real time;

[0045] Step 2. Perform iterative processing on the low-order Hadamard matrix to obtain a high-order Hadamard matrix, convert the row data in the high-order Hadamard matrix into a second observation matrix, and use the second observation matrix to the original image Perform compressed sensing sampling to obtain the high-order measurement value ...

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Abstract

The invention relates to a Hadamard matrix based progressive compressed sensing reconstruction method and system. The method comprises the steps of enabling the original Hadamard matrix to serve as alow-order Hadamard matrix, converting row data in the low-order Hadamard matrix into a first observation matrix, performing compressed sensing sampling on the original image by using the first observation matrix, acquiring a low-order measurement value of the original image, and outputting a reconstructed image generated according to the low-order measurement value to a user to check in real time;performing iteration processing on the low-order Hadamard matrix to obtain a high-order Hadamard matrix, converting row data in the high-order Hadamard matrix into a second observation matrix, performing compressed sensing sampling on the original image by using the second observation matrix, acquiring a high-order measurement value of the original image, and outputting a reconstructed image generated according to the high-order measurement value to the user to check in real time. According to the invention, the image reconstruction effect can be checked in real time, the user is enabled to sense the image compression result, and the expected image compression effect is achieved by sampled data with the lowest sampling rate.

Description

technical field [0001] The invention relates to the field of computer image acquisition and processing, in particular to a progressive compressed sensing reconstruction method and system based on Hadamard matrix. Background technique [0002] In recent years, with the rapid development of computer technology, data and information have also changed people's way of life. With the explosive growth of data, there is a need to change the compression and storage of data. According to the classic Shannon Nyquist sampling law, if you want to To recover the original signal from distortion, the signal sampling frequency needs to be at least twice the bandwidth of the original signal. This sampling law always restricts the compression of signals, but on sparse signals, the compressed sensing theory breaks through this sampling law, so that the present invention can restore the original signal with high quality using a very small amount of sampling data. [0003] Compressed sensing the...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/40
CPCG06T3/4076
Inventor 代锋马宜科赵强张勇东李宏亮
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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