Image reconstruction method based on second-order L0 minimization and edge priori

An edge prior and image reconstruction technology, which is applied in image enhancement, image analysis, image data processing, etc., can solve the problems of low image reconstruction accuracy, poor visual effect, and natural images without edge structure, so as to improve image reconstruction accuracy and visual effects, reducing reconstruction errors, and improving image reconstruction accuracy

Pending Publication Date: 2018-12-18
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

However, natural images do not have obvious edge structures, and contain a lot of low-frequency noise detail structure information
Therefore, how to extract accurate edge structures from complex natural images as prior information provides challenges for image reconstruction
[0007] Based on the above, under the compressed sensing framework, due to the downsampling measurement method, the image reconstruction accuracy is low, and the visual effect is poor, especially when the number of measurements is relatively small. In order to solve the above problems, this case arises

Method used

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  • Image reconstruction method based on second-order L0 minimization and edge priori
  • Image reconstruction method based on second-order L0 minimization and edge priori
  • Image reconstruction method based on second-order L0 minimization and edge priori

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Embodiment 1

[0063] In order to better reflect the advantages of the image reconstruction algorithm based on the second-order L0 minimization and edge prior of the present invention on the reconstruction accuracy, the algorithm described in the present invention and the existing multivariate sampling mechanism based on a specific example will be combined below. Classical algorithms M-OMP, M-BP, M-PFP, M-BAOMP, M-CoSaOMP for comparison.

[0064] The way to compare is: yes Figure 4 In the experimental image shown, the sampling rate is gradually increased from 0.15 to 0.5, and the reconstruction effects achieved by the six algorithms are compared, where the reconstruction effect is represented by PSNR and running time. PSNR is used to measure the accuracy of reconstructed images, and running time is used to measure the reconstruction speed of image reconstruction.

[0065] Figure 5 When the sampling rate increases from 0.15 to 0.5, the comparison chart of the PSNR simulation results of th...

Embodiment 2

[0067] In order to further embody the advantages of the image reconstruction algorithm based on second-order L0 minimization and edge prior in the present invention in terms of reconstruction accuracy, visual effect, and especially the structure of the reconstructed image, the algorithm described in the present invention and the existing one will be combined with a specific example below The classical reconstruction algorithm Tree-CoSaOMP based on prior information, TWSCS, EdgeCS and EOMP are compared.

[0068] The way to compare is: yes Figure 4 In the experimental image shown, the sampling rate is set to 0.4, and the reconstruction effects achieved by the six algorithms are compared, where the reconstruction effects are represented by PSNR, SSIM, running time and visual effects. PSNR and SSIM are used to measure the accuracy of reconstructed images, running time is used to measure the reconstruction speed of image reconstruction, and visual effects are used to measure the a...

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Abstract

The invention discloses an image reconstruction method based on second-order L0 minimization and edge prior. The method comprises the following steps: wavelet transform is carried out on the originalimage, and then the original image is measured by using a multivariable sampling mechanism to obtain all low-frequency data CLL and desampled high-frequency data YLH, YHL, YHH; the low-frequency dataCLL and the zeroed high-frequency wavelet coefficients are transformed by inverse wavelet transform to obtain the low-frequency image XLL. The first-order L0 gradient minimization is used to extract the edge of low-frequency image XLL, and the multivariate prior edge information ELH, EHL, EHH is obtained. The second-order L0 minimization is used to solve the image sparse reconstruction problem under the constraint of multivariate edge priori information, and the reconstruction high-frequency coefficient matrices RecXLH, RecXHL, RecXHH are obtained. The inverse wavelet transform of CLL, RecXLH,RecXHL, RecXHH is used to get the reconstructed image RecX. This method can improve the precision of image reconstruction, and has the characteristics of high precision and good visual effect.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image reconstruction algorithm based on second-order L0 minimization and edge prior, which can be applied to practical engineering fields such as image compression and imaging. Background technique [0002] Under the compressed sensing framework, let the original image be The sparse basis of wavelet transform is ψ∈R N*N , the measurement matrix is ​​φ∈R M*N , then the measurement signal y=φ*ψ*x=Θ*x∈R can be obtained M*1 . Finally, the image reconstruction problem can be solved by the following equation. [0003] [0004] where ‖x‖ 0 is the L0 norm of x, representing the number of non-zero elements in x. [0005] Due to the downsampled measurement method, the results of image reconstruction are generally inaccurate, especially when the number of measurements is relatively small. Moreover, the image sparse reconstruction problem based on the above formula is ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00G06T7/13G06T7/42
CPCG06T7/13G06T7/42G06T11/00G06T2207/20064
Inventor 李丹
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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