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Image restoration method based on low-rank tensor completion and discrete total variation

A restoration method and a total variation technology, applied in the field of image processing, can solve problems such as long restoration time and reduced algorithm efficiency

Active Publication Date: 2019-10-01
ZHEJIANG UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The algorithm has a good repair effect on large damaged areas, but the repair time is too long to reduce the efficiency of the algorithm

Method used

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  • Image restoration method based on low-rank tensor completion and discrete total variation
  • Image restoration method based on low-rank tensor completion and discrete total variation
  • Image restoration method based on low-rank tensor completion and discrete total variation

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Embodiment 1

[0183] The image inpainting method based on low-rank tensor completion and discrete total variation of this application is shown in the algorithm as:

[0184] Input: incomplete tensor Initialize the number of iterations T=0, the maximum number of iterations b, λ, ρ 1 , ρ 2 , ρ 3 and μ ∈ [1, 1.5];

[0185] output: restored tensor

[0186] S6.1, initialization

[0187] S6.2. If T≤b, continue to execute downward; otherwise, output recovery tensor

[0188] S6.3, through formulas (9), (11), (18), (20) to update respectively

[0189] S6.4, update

[0190] S6.5, update

[0191] S6.6, update

[0192] S6.7. Calculate ρ 1 =μρ 1 ,ρ 2 =μρ 2 ,ρ 3 =μρ 3 ;

[0193] S6.8, T=T+1, and return to S6.2.

[0194] S6.1 in the algorithm corresponds to steps 1-3 in the image restoration method, S6.3-S6.7 in the algorithm corresponds to step 4 in the image restoration method, and S6.2, S6. 8 corresponds to step 5 in the image restoration method.

[0195] It should be n...

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Abstract

The invention discloses an image restoration method based on low-rank tensor completion and discrete total variation. The method comprises: introducing a discrete total variation regular term factor and integrating the discrete total variation regular term factor into a unified objective function; providing a method based on low-rank tensor completion and discrete total variation for natural imagerestoration, identifying a to-be-repaired pixel of the input damaged image; establishing the relation between known elements and unknown elements; performing diffusion in different directions on theboundary of the to-be-restored area, diffusing information of the undamaged area into the to-be-restored area to restore the image, and finally obtaining the restored image, so that the image restoration method is smoother in edge processing and more accurate in overall image restoration.

Description

technical field [0001] The application belongs to the field of image processing, and in particular relates to an image restoration method based on low-rank tensor completion and discrete total variation. Background technique [0002] With the rapid development of modern network technology, computer communication and sampling technology, most of the data to be analyzed has a very complex structure. In the process of capturing high-dimensional multi-linear data, some data will be lost. Low-rank tensor completion (LowRank Tensor Completion, LRTC) is based on the low rank of the data set to restore the missing elements. Matrix completion, the second-order tensor completion problem, can effectively estimate the missing value of a matrix from a small sample of known items, and has been applied to the famous Netflix problem. In this problem, we can use a A small set of movie ratings is used to infer user preferences for unknown movies. Matrix completion methods usually assume th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T5/77
Inventor 郑建炜秦梦洁陈婉君徐宏辉路程
Owner ZHEJIANG UNIV OF TECH
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