Color image adaptive reconstruction method based on tensor chain rank

Abstract

The invention relates to a tensor chain rank-based color image adaptive reconstruction method, and the method comprises the following steps: 1, processing a to-be-reconstructed image to generate a reference image and a to-be-complemented high-order tensor; 2, adding different offsets to the first-order tensor chain rank of the initialized tensor chain rank of the high-order tensor to be complemented, and generating m candidate tensor chain ranks; 3, performing minimization processing on each candidate tensor chain rank to obtain m reconstructed tensors, and converting the reconstructed tensor into a reconstructed image; 4, determining the structural similarity between the reconstructed image and the reference image, and selecting a candidate tensor chain rank corresponding to the reconstructed image with the highest structural similarity as a first-order tensor chain rank; 5, repeatedly executing the operations from the step 2 to the step 4 until the structural similarity between the reconstructed image and the reference image is greater than 0.95, and obtaining an optimal reconstructed image. According to the method, the missing image can be reconstructed without an original data structure.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a color image self-adaptive reconstruction method based on tensor chain rank. Background technique [0002] As digital image processing is widely used in communication, medicine, aerospace and other fields, as an important research field of digital image processing, image restoration has gradually become a research hotspot. Since color images are a natural form of tensors, the reconstruction problem of color images can be regarded as a tensor completion problem. [0003] The Tensor Train (TT) decomposition model solves the problem of low efficiency of the rank minimization scheme in capturing tensor global information due to the unbalanced expansion matrix size of the traditional tensor decomposition model by virtue of a more balanced matrixing method. research hotspot in recent years. The tensor chain decomposition model can fully capture the correlation between data ...

AI Technical Summary

Problems solved by technology

In practical problems, the data of the image to be reconstructed is incomplete, and the rank information of the complete image data cannot be obtained, so the current TT-based method is difficult to apply in practical problems

In addition, the existing TT-based methods regard the reconstruction quality of the known data as the reconstruction quality of the overall data with the initial rank randomly obtained, which will cause artifacts that seriously affect the reconstruction quality of the known data, and when the image data is missing When the ratio is too high, the reconstruction quality of the known data cannot represent the reconstruction quality of the entire image

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