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Block Compressed Sensing Method Based on Adaptive Sampling and Smooth Projection

A block-based compressed sensing and adaptive sampling technology, applied in image analysis, image enhancement, instrumentation, etc., can solve the problems of high algorithm complexity and low reconstructed image quality, and achieve reduced algorithm complexity and fast reconstruction speed. , the effect of improving quality

Inactive Publication Date: 2020-12-18
QIQIHAR UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to solve the problems of low quality of reconstructed images and high algorithm complexity in the prior art, and propose a block compression sensing method based on adaptive sampling and smooth projection

Method used

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  • Block Compressed Sensing Method Based on Adaptive Sampling and Smooth Projection
  • Block Compressed Sensing Method Based on Adaptive Sampling and Smooth Projection
  • Block Compressed Sensing Method Based on Adaptive Sampling and Smooth Projection

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specific Embodiment approach 1

[0036] Specific implementation mode 1: The implementation mode of the present invention is based on the adaptive sampling and smooth projection block compression sensing method. The specific process is as follows:

[0037] Basic theory of compressed sensing

[0038] From the perspective of compressed sensing, to recover from M observations And M Φ is an M×N observation matrix, and its sampling rate is R=M / N. The CS theory points out that if x is sufficiently sparse under a certain transformation basis ψ, x can be recovered from y according to the following optimization method.

[0039]

[0040] As long as Φ and ψ are sufficiently uncorrelated, M is large enough. For high-dimensional signals such as image signals, when the sampling operator Φ is stored as a dense matrix, a large amount of storage space will be required. In addition, high dimensionality can make the reconstruction process time-consuming [13] . Therefore, for image sampling and reconstruction, more effic...

specific Embodiment approach 2

[0045] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in the step 1, the original image is set as x [0] , to calculate the adaptive sampling rate of the original image sub-block; the specific process is:

[0046] Calculation of Adaptive Sampling Rate

[0047] Using block-based compressed sensing algorithm (Block-based CS, BCS), the target image signal is first transformed into wavelet domain, and then divided into blocks of the same size but not overlapping. The size of each image block is B×B, using the same observation matrix Φ B Each image sub-block is observed separately. assuming x i is the original input image of the i-th block, then its output block can be expressed as

[0048] the y i = Φ B x i

[0049] In the formula, Φ B is the matrix for M B ×B 2 The observation matrix of Φ B Not associated with image blocks. x i is with B 2 A column vector of samples. In BCS, the entire measurement matrix Φ becomes a ...

specific Embodiment approach 3

[0061] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that the ith high-frequency sub-band (Y) is calculated in the step one or three (i) energy E i ; The specific process is:

[0062] i-th high-frequency sub-band (Y) (i) energy E i The expression is:

[0063]

[0064] In the formula, c(m,n) represents the i-th high-frequency subband (Y) (i) The coefficient at the (m,n) position in the middle; P and Q respectively represent the i-th high-frequency subband (Y) (i) Rows and columns; (m,n) represents the i-th high-frequency subband (Y) (i) location point.

[0065] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

The invention discloses a partitioning compressed sensing method based on adaptive sampling and smooth projection, and relates to a partitioning compressed sensing method. The objective of the invention is to solve the problems of low quality of reconstructed images and high algorithm complexity in the prior art. The method comprises the following steps: 1, setting an original image, and calculating an adaptive sampling rate of an original image subblock; 2, performing Wiener filtering on the obtained original image sub-block with the known adaptive sampling rate to obtain a reconstructed image subjected to the Wiener filtering process; projecting the jth image sub-block in the reconstructed image to obtain an image sub-block; performing direction transformation to obtain transformed imagesub-blocks, and performing smooth projection to obtain smooth projection image sub-blocks; 3, carrying out image reconstruction according to the smooth projection image sub-blocks, judging whether the obtained reconstructed image meets the requirement for the peak signal to noise ratio of the reconstructed image or not, and if the obtained reconstructed image meets the requirement, obtaining thereconstructed image; otherwise, repeatedly executing the second step and the third step until a reconstructed image meeting requirements is obtained. The method is applied to the field of image reconstruction.

Description

technical field [0001] The invention relates to a block-compressed sensing method. Background technique [0002] The traditional sampling theorem requires that the sampling frequency be at least twice the signal bandwidth in order to fully restore the original signal. With the increase of signal bandwidth, the traditional sampling theorem requires higher and higher sampling rate. With the introduction of the concept of compressive sensing (CS), because it allows sampling and compression at the same time, and requires a very low sampling rate, compressed sensing quickly becomes a research hotspot. [1-2] ([1]Candes E J, Romberg J, Tao T.Robustuncertainty principles: exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory,2006,52(2):489-509.[2]Candes E J, Tao T. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies [J]. IEEE Transactions on Information Theory, 2006, 52(12): 5406-5425.). ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06T5/10
CPCG06T5/002G06T5/003G06T5/10G06T2207/10004G06T2207/20064
Inventor 石翠萍苗凤娟陶佰睿靳展黄柏锋王天毅
Owner QIQIHAR UNIVERSITY
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