A system and method for image super-resolution reconstruction utilize standardized group sparsity regularization

A technology of super-resolution reconstruction and image reconstruction, applied in the field of image super-resolution reconstruction system, can solve the problems of obvious differences in statistical characteristics of non-locally similar image blocks, limiting the effective reconstruction of high-frequency texture detail information of images, etc. To achieve the effect of improving the recovery effect and improving the quality

Active Publication Date: 2019-02-15
SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
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Problems solved by technology

Previous methods usually assume that the sparse transformation coefficients have zero-mean statistical properties. However, due to the general non-stationary properties of image signals, the statistical properties of non-loca

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  • A system and method for image super-resolution reconstruction utilize standardized group sparsity regularization
  • A system and method for image super-resolution reconstruction utilize standardized group sparsity regularization
  • A system and method for image super-resolution reconstruction utilize standardized group sparsity regularization

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Embodiment Construction

[0045] Below in conjunction with accompanying drawing and embodiment describe in detail:

[0046] 1. System

[0047] 1. Overall

[0048] Such as figure 1 , the system is provided with an initialization module 10, a routing module 20, an image filtering module 30 and an image reconstruction module 40;

[0049] The input low-resolution image 00, the initialization module 10, the routing selection module 20, the image filtering module 30, the image reconstruction module 40 and the output high-resolution image 50 interact sequentially, and the image reconstruction module 40 interacts with the routing selection module 20.

[0050] In detail: the routing module 20 has two input terminals and one output terminal, and the image reconstruction module 40 has two input terminals and one output terminal;

[0051] An input terminal of the routing selection module 20 interacts with the output terminal of the initialization module 10, and another input terminal of the routing selection mo...

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Abstract

The invention discloses an image super-resolution reconstruction system and a method which are regularized by sparse standardization group, and relates to the technical field of image restoration. Theinvention firstly adopts bilinear interpolation method to obtain initial estimation value of image super-resolution reconstruction; Then, by using the normalized sparse prior of image block group, the quality of super-resolution image reconstruction is improved by iterating the adaptive soft threshold filtering in PCA domain and the regularized least squares in pixel domain of image block group.The system comprises an input low-resolution image (00), an initialization module (10), a routing module (20), an image filtering module (30), an image reconstruction module (40) and an output high-resolution image (50). The image reconstruction module (40) also interacts with the routing module (20). The invention can improve the restoration effect of the high-frequency detail of the image and effectively improve the quality of the super-resolution image reconstruction. The invention is Suitable for video surveillance, medical imaging and other applications.

Description

technical field [0001] The present invention relates to the technical field of image restoration, in particular to an image super-resolution reconstruction system and method using normalization group sparse regularization. Background technique [0002] Image super-resolution aims to restore high-resolution images from low-resolution images, and it has a wide range of applications in medical imaging, digital photography, and computer vision. The traditional interpolation-based super-resolution method is based on the assumption of image continuity, and uses the prior knowledge of similarity of adjacent pixels to generate the gray value of the interpolated pixel by weighting and summing the gray values ​​of adjacent pixels. Interpolation-based super-resolution methods have the advantage of being computationally simple. Traditional interpolation methods include: nearest neighbor interpolation, bilinear interpolation, bicube interpolation, etc. [See literature: [1] Keys R., 'Cu...

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Application Information

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IPC IPC(8): G06T3/40G06T5/00
CPCG06T3/4053G06T5/005
Inventor 熊承义高志荣金鑫
Owner SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
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