Compressed sensing image and video recovery method based on tensor approximation and space-time correlation

A spatiotemporal correlation, compressed sensing technology, applied in image enhancement, image analysis, image data processing and other directions, can solve problems such as unsatisfactory performance, achieve good reconstruction quality and visual effects, improve reconstruction performance, and improve quality. Effect

Pending Publication Date: 2021-03-23
STATE GRID CHONGQING ELECTRIC POWER CO ELECTRIC POWER RES INST +1
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  • Abstract
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  • Application Information

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Problems solved by technology

Although these algorithms have made some progress, the performance is still unsatisfactory because many useful prior information of the signal are still not fully considered

Method used

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  • Compressed sensing image and video recovery method based on tensor approximation and space-time correlation
  • Compressed sensing image and video recovery method based on tensor approximation and space-time correlation
  • Compressed sensing image and video recovery method based on tensor approximation and space-time correlation

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Embodiment 1

[0068] A compressed sensing image and video restoration based on tensor approximation and spatiotemporal correlation, the steps of the restoration method are as follows:

[0069] S1: Material read-in: read-in the material and perform frame processing to form a separate image;

[0070] The material of S1 is a video or a picture. When the material is a video, each frame of image is divided into continuous separate images. When the material is an image, the image is divided into separate images;

[0071] S2: image block: the independent image in step S1 is processed into image block;

[0072] Image block processing is to independently measure and reconstruct each small image block of the same size with the same measurement matrix on a separate image on the basis of compressed sensing; specifically: the size of the image x is N×N, and it is divided by the BCS algorithm Divide into n non-overlapping small blocks B×B, use x k ,k=1,2,...,n means that the compressed samples of the b...

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Abstract

The invention discloses a compressed sensing image and video recovery method based on tensor approximation and space-time correlation, and relates to the technical field of video image processing. Themethod comprises the following steps: S1, material reading: reading a material, and carrying out framing processing to form an independent image; S2, image blocking: performing image blocking processing on the single image in the step S1 to obtain small image blocks; S3, recovery processing: carrying out initial image recovery processing on the small image blocks by adopting a tensor approximation thought; and S4: image reconstruction: carrying out image reconstruction processing on the image after initial image recovery processing to obtain an image block with good quality. According to themethod, a novel recovery model is provided for natural images and videos, and high-quality recovery is realized; and according to the image restoration model, local self-similarity can be fully utilized by extracting similar blocks, and the model can be established by considering low-rank attributes, so that the quality of a reconstructed image is further improved.

Description

technical field [0001] The invention relates to the technical field of video image processing, in particular to compressed sensing image and video restoration based on tensor approximation and spatio-temporal correlation. Background technique [0002] Traditional image and video sampling and reconstruction are constrained by the Nyquist sampling theory, which requires the sampling rate to be at least twice the signal bandwidth. Due to the redundancy in the signal, there is always a computational complexity issue. In order to effectively eliminate transmission redundancy and reduce the amount of calculation, compressive sensing technology breaks the limitation of Nyquist sampling theory and has aroused great interest. CS only needs to obtain a small number of measured values ​​through projection to achieve reconstruction. More specifically, according to CS theory, signals with sparse representations in certain domains can be recovered with high probability from these measur...

Claims

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Application Information

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IPC IPC(8): G06T17/00G06T5/00
CPCG06T17/00G06T2207/10016G06T2207/20021G06T2207/20221G06T5/77
Inventor 向菲尹心王晋宇陈涛江金洋程晓宫林余亚玲李俊杰张静仲元红钱基业
Owner STATE GRID CHONGQING ELECTRIC POWER CO ELECTRIC POWER RES INST
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