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Image compression reconstruction method under compressed sensing frame based on non-convex model

A compressed sensing and image compression technology, applied in the field of image processing, which can solve the problems of slow speed, low image reconstruction accuracy, and large storage capacity.

Inactive Publication Date: 2012-08-08
XIDIAN UNIV
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Problems solved by technology

[0011] 1. Under the compressed sensing framework based on l 1 The compression reconstruction method StOMP of the norm model, the image reconstruction accuracy is not high
[0012] 2. The non-convex model-based compression reconstruction method FOCUSS under the compressed sensing framework has too much storage capacity and is too slow to be used for image compression reconstruction.

Method used

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  • Image compression reconstruction method under compressed sensing frame based on non-convex model
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  • Image compression reconstruction method under compressed sensing frame based on non-convex model

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Embodiment Construction

[0086] see figure 1 , the concrete implementation steps of technical scheme 1 of the present invention are as follows:

[0087] Step 1, get the coefficient column vector x of the original image I:

[0088] (1.1) According to the size N of the original image I and the required image compression rate r, draw the data volume that needs to obtain M=rN from the original image I, wherein N equals the product of the number of rows and the number of columns of the original image;

[0089] (1.2) do two-dimensional wavelet transform to original image I, obtain coefficient matrix W, this wavelet transform can also adopt any kind of image sparse transform, as discrete cosine transform or curvelet transform;

[0090] (1.3) Set the coefficient threshold according to the coefficient matrix:

[0091] (1.3a) Arrange the coefficient matrix W into a column vector α in descending order of magnitude;

[0092] (1.3b) Calculate the coefficient threshold value: μ=α(κ), where κ=M / 5, and the value r...

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Abstract

The invention discloses an image compression reconstruction method under a compressed sensing frame based on a non-convex model, which is used for mainly solving the problems that a carbon steel (CS) non-convex model based on a linear programming (lp) norm has a large computing memory space and a low arithmetic speed in the image compression reconstruction, and a project is difficult to implement. The method is implemented throguh the following steps: transforming images to obtain coefficients of a transform domain; acquiring compressed data by carrying out Fourier transform on the coefficients of the transform domain and random selection; adopting a gradient projection method for the compressed data to calculate descent direction and descent step size to realize updating iteration and optimizing solution, and reconstructing the coefficients of the transform domain; and carrying out inverse transformation on the reconstructed coefficients of the transform domain to obtain reconstructed images. The method provided by the invention is simple in compression and high in reconstruction accuracy, is only required to carry out operations of fast Fourier transform and vector dot product in the reconstruction process, is used for successfully achieving mass storage, has a very fast reconstruction speed, and can be applied to image compression encoding.

Description

technical field [0001] The invention belongs to the field of image processing, relates to image compression and reconstruction under the framework of compressed perception, and can be used for image and video compression encoding. technical background [0002] With the continuous development of compressive sensing CS theory, its application has gradually penetrated into all walks of life. In the field of image processing, people have begun to study image / video compression coding methods based on compressive sensing theory. The more widely used at this stage is based on l 1 CS compression reconstruction model of norm minimization theory: [0003] min x I ^ R n | | x | | l 1 , ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/14H04N7/26G06T9/00H04N19/645
Inventor 赵光辉王正杨石光明吴伟佳陈旭阳沈方芳张天键
Owner XIDIAN UNIV
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