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

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 ...

AI Technical Summary

Problems solved by technology

[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 the texture information of the image is not preserved perfectly, and provides a high-dimensional image reconstruction method based on tensor dictionary and full variation noise method

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