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Image Restoration Method and System Based on Multiple Regularization of Equation Structure

An image restoration and equation technology, applied in the direction based on specific mathematical models, image enhancement, image data processing, etc., can solve problems such as unusable, multiple regularization BCS methods have not yet appeared, achieve fast convergence speed, avoid covariance matrix , the effect of high reconstruction accuracy

Active Publication Date: 2022-08-05
HARBIN ENG UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Single sparse transformation can be realized by modifying the linear model through the comprehensive method, but for irreversible sparse transformation, or to introduce multiple sparse transformations, the comprehensive method cannot be used
Therefore, in the field of complex image restoration algorithms, the multiple regularization BCS method with a complete theoretical basis and wide applicability has not yet appeared.

Method used

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  • Image Restoration Method and System Based on Multiple Regularization of Equation Structure
  • Image Restoration Method and System Based on Multiple Regularization of Equation Structure
  • Image Restoration Method and System Based on Multiple Regularization of Equation Structure

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Embodiment 1

[0060] like figure 1 As shown, the image restoration method based on the multiple regularization of the equation structure provided by this embodiment introduces multiple sparse transformations through the equation structure, that is, introduces multiple independent Delta probability density functions, and establishes multiple The restricted multi-level Bayesian model gives a sparsity assumption for the transformation coefficients corresponding to each transformation, so as to establish the conjugate matching relationship between the layers, and finally combines the conjugate gradient method with the Expectation maximization (EM) method. (conjugate gradient method, CGM) iteratively achieves fast reconstruction of the target image.

[0061] The image restoration method based on multiple regularization of the equation structure includes the following steps:

[0062] Step (1): On the premise of known measurement matrix and observation vector, establish a linear equation for imag...

Embodiment 2

[0171] This embodiment provides an image restoration system based on multiple regularization of the equation structure, including:

[0172] The image receiving module is used for receiving the to-be-restored image, converting the image into a one-dimensional signal, and projecting it onto the observation vector with the measurement matrix;

[0173] The image restoration module is used to input the measurement matrix and the observation vector into the image restoration model based on the multiple regularization of the equation structure to obtain the mean value of the target image; wherein, the image restoration model uses the conjugate gradient method to calculate the target according to the measurement matrix and the observation vector. Image mean and intermediate variables; use the calculated intermediate variables and target image mean to update the hyperparameter vector and equation variance of Bayesian compressed sensing; judge whether the convergence conditions are met, ...

Embodiment 3

[0175] This embodiment also provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and executed on the processor, and when the computer instructions are executed by the processor, the steps of the method described in Embodiment 1 are completed. .

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Abstract

The invention discloses an image restoration method and system based on multiple regularization of an equation structure. The equation structure is introduced to construct a measured Gaussian likelihood function; based on the constructed measured Gaussian likelihood function, a plurality of sparse transformations are selected, and the sparse transformation coefficient is On the basis, multiple independent probability density functions are given to calculate the conditional prior probability density of the target image; based on the conditional prior probability density of the target image, the mean estimation model of the target image is calculated according to the Bayes rule; Combined with conjugate gradient method, iteratively realizes fast reconstruction of target image, overcomes the invertibility limitation of sparse representation introduced by synthesis method, and allows the use of any number of sparse transformations, and the joint sparse domain can effectively improve the convergence speed of the algorithm.

Description

technical field [0001] The invention belongs to the field of image restoration, and in particular relates to an image restoration method and system based on multiple regularization of an equation structure. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] As a sparse reconstruction technology, compressive sensing (CS) is widely used in various fields, such as radar and medical imaging, seismic imaging, image processing, and so on. According to CS theory, the original signal can be reconstructed with high precision with the sampling data volume much smaller than that required by the Nyquist-Shannon sampling law, provided that the signal meets the requirement of sparsity. But more strikingly, CS also has excellent performance in high-accuracy reconstruction and estimation of signals. In order to take into account the reconstruction accuracy...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06N7/00G06K9/62G06V10/84G06V10/77
CPCG06T5/001G06N7/01G06F18/2136
Inventor 郭企嘉辛志男周天李海森
Owner HARBIN ENG UNIV
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