Compressed perceptual image reconstruction algorithm based on depth learning

A technology of compressed sensing and image reconstruction, which is applied in the field of image processing, can solve the problems of complex calculation and time-consuming, and achieve the effect of good reconstruction accuracy

Active Publication Date: 2019-03-01
HUBEI UNIV OF TECH
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Problems solved by technology

However, almost all of these methods are computationally complex and time-consuming when solving the problem of image reconstruction.

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  • Compressed perceptual image reconstruction algorithm based on depth learning
  • Compressed perceptual image reconstruction algorithm based on depth learning
  • Compressed perceptual image reconstruction algorithm based on depth learning

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Embodiment Construction

[0029] The present invention will be further explained below in conjunction with specific embodiments.

[0030] A compressed sensing image reconstruction algorithm based on deep learning, the method steps are as follows:

[0031] S1: Data preparation stage, preprocessing the data, including extracting the gray value of the data, and processing the image into blocks;

[0032] 1-1) Select 100 images, 90 of which are used as the training set and 10 as the test set, and all the training images are converted into grayscale images, and only the brightness information of the image is extracted. During the test, if it is an RGB color image, It can be divided into R, G, B3 channels, which are tested separately in turn.

[0033] 1-2) If the image is too large, the network structure will be too large, the training will be complicated, and it will be easy to overfit, so divide each image into 33×33 small blocks without overlapping. Because the size of the pictures in the data set is dif...

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Abstract

The invention relates to a compression perception image reconstruction algorithm based on depth learning. The method comprises the following steps: S1, preprocessing image data, including extracting gray value of the data and dividing the image into blocks; S2, measuring the segmented image blocks to obtain a measurement matrix; S3: Constructing a 10-layer deep compression perceptual reconstruction network; S4, training the 10-layer network in the depth learning framework; S5, after passing through that depth neural network, obtain the reconstructed image block, and rearranging the image blockaccording to the original row and column value accord to the index; S6, after that image blocks are rearrange to obtain a reconstructed image, a BM3D denoiser is selected to carry out denoising processing on the image, and finally the reconstructed image is obtained. The compression perception image reconstruction algorithm provided by the invention consumes most of time in the network training stage, and the image reconstruction speed is very fast after the network training is completed. The invention replaces the traditional reconstruction algorithm through the depth learning network, but still has good reconstruction accuracy.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a compression sensing image reconstruction algorithm based on deep learning. Background technique [0002] Compressed sensing theory can obtain signal measurement results at a sampling rate much lower than Nyquist sampling law, and in a specific sparse domain, the original signal can be restored with high quality. Compressed sensing theory mainly includes three steps: signal sparse representation, signal observation and signal reconstruction, and its mathematical model is [0003] y=Φx (1) [0004] The process of recovering the original signal x from y is the most critical part of compressed sensing - the reconstruction of the signal, its essence is to solve an l 0 The smallest norm problem: [0005] min||x|| 0 s.t.y = Φx (2) [0006] Greedy algorithms, convex optimization algorithms, and Bayesian-like algorithms have been used to reconstruct images in compr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T1/00H03M7/30G06N3/04
CPCH03M7/3062G06T1/0007G06N3/045
Inventor 曾春艳叶佳翔武明虎马超峰吕松南朱栋梁王正辉
Owner HUBEI UNIV OF TECH
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