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A Wavelet Sparse Basis Optimization Method Based on Compressed Sensing in Image Reconstruction

An image reconstruction and compressed sensing technology, which is applied in 2D image generation, image enhancement, image analysis, etc., can solve problems such as low precision and insufficient coefficient sparsity, and achieve data reconstruction, detail reconstruction capability enhancement, and compensation The effect of texture image reconstruction accuracy is not high

Active Publication Date: 2020-06-19
INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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Problems solved by technology

[0008] The technical problem to be solved by the present invention is as follows: Aiming at the insufficient sparsity of the coefficients in the wavelet domain after the existing wavelet transform in the reconstruction of the compressed sensing signal and the problem of low precision after the reconstructed signal, a suppression matrix for the wavelet transform is constructed, Make the wavelet coefficients more sparse

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  • A Wavelet Sparse Basis Optimization Method Based on Compressed Sensing in Image Reconstruction
  • A Wavelet Sparse Basis Optimization Method Based on Compressed Sensing in Image Reconstruction
  • A Wavelet Sparse Basis Optimization Method Based on Compressed Sensing in Image Reconstruction

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[0036] The present invention will be further described below in conjunction with the detailed description of the accompanying drawings.

[0037] Depend on figure 2 It can be seen from the schematic diagram in the figure that the original wavelet sparse transformation basis is Ψ 0 ’, assuming that the suppression matrix is ​​w, the final sparse transformation is, wΨ 0 ’x=s, put wΨ 0 ’ as the improved wavelet sparse transformation basis, that is, Ψ’ in the schematic diagram. The grayscale image of Lena (512*512) and Fingerprint (512*512) is used to do the simulation experiment of image reconstruction through MATLAB respectively. Since the image size is too large, the images are reconstructed in columns, and then spliced ​​into the reconstructed image. full frame image. Since the length of a single data is 512, the size of the suppression matrix is ​​512*512, the first item of the diagonal element is 1, and the tolerance is an arithmetic sequence of -1 / 512, so the number of w...

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Abstract

The invention relates to a method for wavelet sparse base optimization based on compressed sensing in image reconstruction, that is, optimization is performed on a wavelet sparse base by using a suppression matrix. According to the invention, firstly multilayer wavelet transformation is performed on original signal data, coefficients in a wavelet domain are observed, and the coefficients present a gradually decreasing trend on the whole, so that a suppression matrix is designed to suppress the small coefficients so as to achieve a purpose of enabling the coefficients to be sparser. The suppression matrix becomes a part of a wavelet transformation base. Simulation results show that the quality improving effect of a reconstructed image is the best when the sampling rate is within a range of 0.15 to 0.45, and the peak signal to noise ratio is improved by about 0.5dB to 1dB. The method also has good effects for reconstruction of fingerprint type texture images, thereby making up a defect of low reconstruction precision of wavelet transformation based compressing sensing for the texture images to a certain degree.

Description

technical field [0001] The invention relates to a wavelet sparse basis optimization method in image reconstruction based on compressed sensing, which is characterized in that the original signal data of the signal is recovered and reconstructed with 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 measurement process ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06T11/00
CPCG06T5/00G06T11/00G06T2207/20064
Inventor 魏子然徐智勇张健林吴润泽唐惜
Owner INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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