Low-rank tensor completion method for alpha-order total variation constraint of damaged video

A fully variable and broken technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as blurred edges of structural fine-grained texture areas, repair images, loss of affine details, etc., and achieve the effect of overcoming the oscillation phenomenon

Pending Publication Date: 2021-06-18
XIAN UNIV OF TECH
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Problems solved by technology

[0007] The existing LRTV is not enough to use the non-local and fine-grained structural information of tensors to repair images, which will lead to blurred edges and loss of affine details in areas with complex structures and fine-grained textures.

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  • Low-rank tensor completion method for alpha-order total variation constraint of damaged video
  • Low-rank tensor completion method for alpha-order total variation constraint of damaged video
  • Low-rank tensor completion method for alpha-order total variation constraint of damaged video

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Embodiment

[0137] The following will use YUV video data to illustrate the present invention's low-rank tensor completion method for the α-order total variation constraints of damaged video to further illustrate its effect:

[0138] The experimental data comes from YUV video sequences, and the video data are suzie and hall_qcif respectively. The experimental video data is read into MATLAB, some commonly used 4:2:0 YUV format video test sequences are used, and the first 100 frames are selected as the experimental data, so the data size is 176×144×100, they can be regarded as a 3D tensor. By randomly masking a part of the original tensor data in all channels of the experimental video data, the remaining pixels are used to form a damaged 3D tensor to complete the tensor Among them, the data loss rate of the experimental video is 95% and 75%. Simultaneously convert the three-dimensional tensor Expand along each module into a two-dimensional expansion matrix Here N=3, the sizes of the...

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Abstract

The invention discloses a low-rank tensor completion method for alpha-order total variation constraint of a damaged video, and the method is implemented according to the following steps: reading in a damaged video file with a high loss rate by utilizing MATLAB, processing the damaged video file into a three-dimensional tensor, expanding the three-dimensional tensor into a two-dimensional matrix along each module, performing regularization processing on boundaries of the matrixes, and obtaining a two-dimensional matrix of a zero Dirichlit boundary condition; defining a target functional about tensor completion of the damaged video, wherein the target functional comprises an alpha-order total variation regular constraint term and a low-rank constraint term, and the alpha-order total variation regular constraint term and the low-rank constraint term are not independent of each other; introducing three auxiliary matrixes for the boundary-regularized two-dimensional matrix, decoupling and optimizing a target functional through an augmented Lagrange formula, solving the optimized target functional, and finally obtaining a complemented three-dimensional video tensor through continuous iteration; and combining a fractional order TV regularization term in a fractional order bounded variation space with low-rank constraint to carry out tensor repair, so global information can be recovered, and lost fine details can also be recovered.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and relates to a low-rank tensor completion method for α-order full variation constraints of damaged videos. Background technique [0002] Digital image inpainting refers to techniques that reconstruct completely missing / broken parts of an image or remove unwanted objects of interest in an imperceptible manner. In recent years, with the rapid development of data acquisition technology, a large number of multi-channel visual data sets have been collected in many areas of social production and life, such as: RGB images, digital videos, multi-spectral and hyperspectral images, etc. Among them, video data sets The scale and quantity of video are increasing day by day, especially in daily life, digital video occupies a very important position. However, due to the influence of transmission or compression, some information in video data is often lost or damaged. When the loss rate is h...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/10016G06T5/77Y02T10/40
Inventor 杨秀红薛怡许鹏石程金海燕
Owner XIAN UNIV OF TECH
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