Tensor repairing method based on non-local self-similarity and low-rank regularization of tensors

A repair method, self-similar technology, applied in the field of image processing, can solve the problem of ignoring non-local self-similar information and so on

Inactive Publication Date: 2019-09-10
李晓彤
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[0008] Although methods that exploit tensor local smoothness priors have achieved goo

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  • Tensor repairing method based on non-local self-similarity and low-rank regularization of tensors
  • Tensor repairing method based on non-local self-similarity and low-rank regularization of tensors
  • Tensor repairing method based on non-local self-similarity and low-rank regularization of tensors

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Embodiment Construction

[0058] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0059] At first the symbols used in the present invention are described:

[0060] In the present invention, a vector is represented by a lowercase letter (a), a matrix is ​​represented by an uppercase letter (A), and a swash letter is used to represent tensors. The following sections introduce some basic concepts and knowledge of tensors.

[0061] (1) Basic knowledge of tensor

[0062] Define a rank N tensor where its (i 1 , i 2 ,...,i N ) position element is defined as tensor The expansion matrix along the nth direction is defined as where the tensor in (i 1 , i 2 ,...,i N ) po...

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Abstract

The invention discloses a tensor repairing method based on non-local self-similarity and low-rank regularization of tensors. The method comprises the following steps: S1, establishing a tensor model;S2, optimizing a target function according to the adjacent operator function, and solving the tensor model; and S3, carrying out iterative solution by utilizing a rank growth strategy. A plug-and-playframework is utilized to design a non-explicit non-local self-similarity rule to promote detail recovery of the tensor, a model solving algorithm based on a block continuous upper bound descent method is designed. Numerical experiment shows that the proposed model NLS-LR has obvious advantages in the aspects of structure, contour, details and the like of the recovery target tensor, and experimental results show that the visual effect and evaluation indexes of the model all exceed those of a plurality of existing mainstream methods.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a tensor restoration method based on tensor non-local self-similarity and low-rank regularization. Background technique [0002] In today's information society, information is exploding. In real life, data such as magnetic resonance images (MRI), hyperspectral images / multispectral images (HSI / MSI), color pictures and videos often have high-dimensional structures. As a generalization of vectors and matrices, tensors play a very important role in representing high-dimensional data. Due to the lack of information or the high cost of obtaining information, tensors in real life often show an incomplete structure. The problem of inferring a complete tensor from a missing tensor is called the tensor repair problem (LRTC). The tensor inpainting problem has a wide range of applications in reality, such as image inpainting, nuclear magnetic resonance image restoration, rain rem...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/002G06T5/005G06T2207/10024
Inventor 李晓彤
Owner 李晓彤
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