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An Adaptive Compressed Sensing Sampling Method Based on Image Information Entropy

A technology of compressed sensing and image information, which is applied in the field of image processing and can solve the problems of low sampling and reconstruction efficiency.

Inactive Publication Date: 2016-06-29
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Compared with the existing method of uniformly sampling all image blocks, the present invention has the characteristics of combining actual sampling with specific image features, and can overcome the low sampling and reconstruction efficiency caused by ignoring the internal texture features of the image in the traditional method Shortcomings

Method used

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  • An Adaptive Compressed Sensing Sampling Method Based on Image Information Entropy
  • An Adaptive Compressed Sensing Sampling Method Based on Image Information Entropy

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Experimental program
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Embodiment Construction

[0064] The present invention mainly adopts the mode of emulation experiment to verify the feasibility of this system model, and all steps, conclusion are verified correctly on MATLAB7.11, and concrete implementation steps are as follows:

[0065] Step 1, preprocessing of the original image

[0066] Set m=16, divide the original image with a resolution of C×R into N=(C×R) / 16 non-overlapping square image blocks with a size of 16×16 according to the traditional image block method , denoted as B 1 , B 2 ,...,B i ,...,B N , where C represents the width of the original image, R represents the height of the original image, N represents the number of generated image blocks, m represents the width or height of each square image block generated, i represents the index of the image block, i ∈{1,2,...,N};

[0067] Step 2, calculate the total number of samples sampled for the entire image

[0068] The traditional compressed sensing sampling rate for the entire image is recorded as r,...

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Abstract

The present invention provides an adaptive compressed sensing sampling method based on image information entropy, which distributes sampling samples to each image block adaptively: during compressed sensing sampling, more image blocks with large information entropy are allocated Sample observation value, for the image block with small information entropy, less sample observation value is allocated. After the sampling at the encoding end is completed, the number of sampling samples allocated to each image block is transmitted to the decoding end, and the number of samples allocated to each image block is quantized. At the decoding end, after receiving the quantized sample number information, a sampling matrix for each image block is generated to achieve high-performance compressed sensing sampling and reconstruction for the entire image signal. Compared with the existing method of uniformly sampling all image blocks, the present invention has the characteristics of combining actual sampling with specific image features, and can overcome the low sampling and reconstruction efficiency caused by ignoring the internal texture features of the image in the traditional method Shortcomings.

Description

technical field [0001] The invention belongs to the field of image processing, and mainly relates to digital image compression and reconstruction technology. Background technique [0002] Images usually come from natural scenes, and the shooting and recording process of any image is essentially a process of sampling and compressing the original scene data. For a long time, in order to completely reconstruct the signal, the traditional sampling process must follow the Nyquist sampling theorem. This sampling theory has been used in the field of signal processing for nearly eighty years, and it was not until the compressed sensing theory was proposed that this sampling criterion was broken. In 2006, Donobo, Candes and Tao et al. proposed the compressed sensing theory for sparse signal processing. According to this theory, the signal can be randomized at a sampling frequency much lower than that required by the Nyquist sampling theorem. Sampling to obtain a small number of obs...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/50
Inventor 朱树元曾兵
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA