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Structural lack image filling method based on low rank matrix reconstruction

A filling method and low-rank matrix technology, applied in the field of computer vision, can solve problems such as unprocessable and missing image filling methods

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Problems solved by technology

The technical scheme adopted by the present invention is, based on the structural missing image filling method of low-rank matrix reconstruction, combining the matrix reconstruction theory with the sparse representation theory, and introducing a dictionary learning model on the basis of the traditional matrix reconstruction model, thereby solving the existing problems unhandled problem

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  • Structural lack image filling method based on low rank matrix reconstruction
  • Structural lack image filling method based on low rank matrix reconstruction
  • Structural lack image filling method based on low rank matrix reconstruction

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Embodiment Construction

[0048] The dictionary learning model is introduced on the basis of the traditional matrix reconstruction model, so that the structurally missing low-rank matrix can be reconstructed to obtain the filled image, that is, the structural missing image filling method based on low-rank matrix reconstruction, so as to solve the existing problems. Problems that technology cannot handle. The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0049] 1) Considering the image as a matrix, the original image can be represented by a matrix A. To solve the problem of image filling with structural missing pixels is to solve the following optimization equation:

[0050] min||A|| * +λ||B|| 1

[0051] (1)

[0052] Constraints A=ΦB,A+E=D,P Ω (E)=0

[0053] ||A||* Indicates the kernel norm of matrix A. ||·|| 1 Indicates the one-norm of the matrix. Ω is the observation space, p Ω (·) is a projection operator, whi...

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Abstract

The invention belongs to the computer vision field and aims at realizing filling to pixel structural lack images. The structural lack image filling method based on low rank matrix reconstruction comprises the following steps: 1) taking the image as a matrix, using a matrix A to represent an original image and converting a pixel structural lack image filling problem into a solving optimization equation; 2) training a dictionary fai; 3) using an alternative direction method ADM to convert a sequence into a sequence so as to carry out solving and then carrying out iteration solving according to steps so as to acquire a final result. The method in the invention is mainly used for computer image processing.

Description

technical field [0001] The invention belongs to the field of computer vision. In particular, it relates to methods for filling structurally missing images based on low-rank matrix reconstruction. Background technique [0002] The matrix reconstruction problem, including matrix filling and matrix recovery, has attracted much attention since it was proposed, and has strong vitality. Especially in the era of big data, it has become a research hotspot in the fields of mathematics and computers. In recent years, there have been many research results on algorithms to solve the matrix reconstruction problem. These algorithms mainly use the iterative singular value decomposition method to approximate the solution of the original model. Such as SVT (singular value threshold) algorithm, APG (accelerated neighbor gradient) algorithm, ALM (augmented Lagrangian multiplier) algorithm, etc. Among the existing algorithms, when solving the matrix filling problem, the SVT algorithm requir...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 杨敬钰杨雪梦叶昕辰
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