High-dimensional image de-noising method based on tensor dictionary and total variation

A full variation and dictionary technology, applied in the field of digital image processing, can solve the problems of incomplete retention of image texture information, failure to consider band correlation, etc., and achieve the effect of improving clarity and perfection

Active Publication Date: 2018-08-14
GUILIN UNIV OF ELECTRONIC TECH
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[0004] What the present invention aims to solve is that the existing high-dimensional image reconstruction method does not consider the correlation between bands, and the problem that

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  • High-dimensional image de-noising method based on tensor dictionary and total variation

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[0034] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific examples and with reference to the accompanying drawings.

[0035] A high-dimensional image denoising method based on tensor dictionary and total variation, such as figure 1 As shown, the specific steps are as follows:

[0036] Step 1. Input the noisy multispectral image Where d W =512,d H =512,d S =31.

[0037] Step 2. Add noise image Divide into blocks to obtain several full-band image blocks.

[0038] Step 3. Use k-means++ (improved K-means clustering algorithm) to cluster the stereo image blocks in step 2 to obtain K-type similar full-band image blocks, and use k-th similar full-band image blocks ( tensor composed of fullband patches, FBP) In the k-means++ algorithm, the value of the number of clusters K can be determined artificially, but in order to ensure the accuracy of clas...

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Abstract

The invention discloses a high-dimensional image de-noising method based on a tensor dictionary and total variation. Based on the study of high-dimensional image processing, the tensor dictionary learning combines a total variation regular term, a high-dimensional image de-noising model combing the tensor dictionary learning with a TV regular term is provided, and then the model is solved by usingan alternate iterative method to obtain a reconstructed MSI image after iteratively updating. The high-dimensional image de-noising method based on the tensor dictionary and the total variation has the advantages that a high-dimensional image is regarded as a tensor entirety, stereochemical structure information cannot lose, the correlation among wave bands is also considered, and the accuracy ofan algorithm is improved through a tensor dictionary learning mode; under the premise without losing a high-dimensional image space structure, a high-order TV regular term is adopted, more complete edge information is well preserved, and a good reconstruction effect is obtained; the experimental result achieves a better effect in both subjective vision and objective evaluation indexes, and more texture information and contour information can be retained.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to a high-dimensional image denoising method based on a tensor dictionary and full variation. Background technique [0002] The scope of image processing has gradually expanded from two-dimensional images to three-dimensional and even high-dimensional images. During the transmission process, the image quality will be degraded due to various noise interference. How to recover high-quality high-quality images from the noise map? Dimensional images have become one of the research hotspots in recent years. [0003] Aiming at this problem, many literatures have proposed some different solutions. For example, Wang proposed to use group low-rank representation to denoise hyperspectral images, (References: M.Wang, J.Yu, J.H.Xue, and et al, "Denoising of hyperspectral images using group low-rank representation," IEEEJournal of Selected Topics in Applied Earth Observations ...

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Application Information

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IPC IPC(8): G06T5/00G06K9/62
CPCG06T5/002G06T2207/20021G06F18/28G06F18/23213
Inventor 陈利霞杨彬王学文欧阳宁首照宇莫建文林乐平
Owner GUILIN UNIV OF ELECTRONIC TECH
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