Non-local low-rank conversion domain and full-connection tensor decomposition image reconstruction method and device

A technology of tensor decomposition and image reconstruction, which is applied in the field of image processing, can solve problems such as high requirements and difficulty in obtaining computer computing power for samples, and achieve the effect of accurate image restoration

Active Publication Date: 2022-03-01
ZHEJIANG LAB +1
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Problems solved by technology

However, methods based on deep learning require a large number of labeled samples, which are difficult to obtain and require high computing

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  • Non-local low-rank conversion domain and full-connection tensor decomposition image reconstruction method and device
  • Non-local low-rank conversion domain and full-connection tensor decomposition image reconstruction method and device
  • Non-local low-rank conversion domain and full-connection tensor decomposition image reconstruction method and device

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Embodiment Construction

[0036] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0037] Such as Figure 1 to Figure 4 As shown, a non-local low-rank transformation domain and fully connected tensor decomposition image reconstruction method includes the following steps:

[0038] S1, input the image to be repaired;

[0039] Determine the area of ​​the image to be repaired, and divide the pixels in the image into known points and unknown points. Known points are points in the image whose pixel value is not 0, and unknown points are points in the image whose pixel value is 0. Unknown points As the area to be repaired in the image; all unknown points in the image form a set;

[0040] input broken image , determine the region of the image to be...

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Abstract

The invention discloses a non-local low-rank conversion domain and full-connection tensor decomposition image reconstruction method and device. The method comprises the following steps: S1, inputting a to-be-restored image; s2, constructing a tensor decomposition model: S2.1, segmenting the input image to obtain non-local tensor blocks; s2.2, introducing the non-local tensor block into a B spline conversion domain to obtain a conversion domain form of the non-local tensor block; s2.3, through the non-local tensor blocks, constructing a non-local similar tensor block group; s2.4, combining full-connection tensor decomposition, and constructing a full-connection tensor decomposition factor; s2.5, constructing a low-rank tensor completion model, and performing optimization according to the steps S2.1 to 2.4 to obtain a non-local low-rank conversion domain and full-connection tensor decomposition-based model; and S3, constructing an image restoration model, obtaining a to-be-restored image, and obtaining a restored image through a restored image tensor block group obtained through the tensor decomposition model. In the spectral image restoration process, the whole image reconstruction is more accurate.

Description

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

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/005G06T2207/20081
Inventor 鲍虎军杨非华炜秦梦洁傅家庆郑建炜
Owner ZHEJIANG LAB
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