Non-local low-rank transform domain and fully connected tensor decomposition image reconstruction method and device

Abstract

The invention discloses a non-local low-rank transformation domain and a fully connected tensor decomposition image reconstruction method and device, including: S1, inputting an image to be repaired; S2, constructing a tensor decomposition model, including: S2.1, segmenting the input image, Obtain the non-local tensor block; S2.2, introduce the non-local tensor block into the B-spline transformation domain, and obtain the transformation domain form of the non-local tensor block; S2.3, construct the non-local similarity through the non-local tensor block Tensor block group; S2.4, combine fully-connected tensor decomposition to construct fully-connected tensor decomposition factors; S2.5, construct a low-rank tensor completion model, and optimize it according to S2.1‑2.4 to obtain a Local low-rank transformation domain and fully connected tensor decomposition model; S3, construct an image inpainting model, obtain the image to be inpainted, and obtain the inpainted image through the inpainted image tensor block group obtained by the tensor decomposition model. This makes the image reconstruction more accurate in spectral image restoration.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image reconstruction method and device based on non-local low-rank transform domain and fully connected tensor decomposition. Background technique [0002] Due to factors such as manufacturing process, device aging, or transmission errors, high-dimensional image data may lose pixels during the capture and generation process. Low Rank Tensor Completion (LRTC) is to recover lost elements according to the low rank of the dataset. Matrix completion is a second-order tensor completion method, which usually assumes that the matrix is ​​of low rank, and uses this as a constraint to minimize the difference between a given incomplete matrix and an estimated matrix. However, when the data to be analyzed has a complex structure, the use of matrices to describe high-dimensional data has problems such as the curse of dimensionality, overfitting, and incomplete data struct...

AI Technical Summary

Problems solved by technology

However, methods based on deep learning require a large number of labeled samples, which are difficult to obtain, and a large number of labeled samples require high computer computing power, so the research and application of small-sample traditional methods are still necessary and have great potential. room for improvement

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