Image restoration method and system based on equality structure multiple regularization

An image restoration and equation technology, applied in the direction based on specific mathematical models, image enhancement, image data processing, etc., can solve the problem that the multiple regularization BCS method has not yet appeared and cannot be used, and achieves avoiding covariance matrix and fast convergence speed. , the effect of reducing memory requirements and computational effort

Active Publication Date: 2021-08-06
HARBIN ENG UNIV
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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 compr

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  • Image restoration method and system based on equality structure multiple regularization
  • Image restoration method and system based on equality structure multiple regularization
  • Image restoration method and system based on equality structure multiple regularization

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Embodiment 1

[0060] Such as figure 1 As shown, the image restoration method based on 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 sparsity assumptions for the transformation coefficients corresponding to each transformation, so as to establish the conjugate matching relationship between each level, and finally combine the conjugate gradient method with the Expectation maximization (EM) method (conjugate gradient method, CGM) iteratively achieves fast reconstruction of the target image.

[0061] An image restoration method based on multiple regularization of an equation structure, comprising the following steps:

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

Embodiment 2

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

[0172] An image receiving module, which is used to receive the image to be restored, convert the image into a one-dimensional signal, and project the measurement matrix to the observation vector;

[0173] An image restoration module, which is used to input the measurement matrix and observation vector into the image restoration model based on 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 value according to the measurement matrix and the observation vector Image mean and intermediate variable; use the calculated intermediate variable and target image mean to update the hyperparameter vector and equation variance of Bayesian compressive sensing; judge whether the convergence condition is satisfied, and if s...

Embodiment 3

[0175] This embodiment also provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and run on the processor. 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 equality structure multiple regularization, and the method comprises the steps: introducing an equality structure, and constructing a measurement Gaussian likelihood function; selecting a plurality of sparse transformations based on the constructed measurement Gaussian likelihood function, and giving a plurality of mutually independent probability density functions based on sparse transformation coefficients to calculate the conditional prior probability density of the target image; based on the condition prior probability density of the target image, calculating a mean value estimation model of the target image according to a Bayesian rule; and finally, achieving fast reconstruction of the target image by combination of an expectation maximization method and a conjugate gradient method iteration so that the reversibility limitation of sparse representation introduced by a comprehensive method is overcome, sparse transformation of any number is allowed to be adopted, and the convergence speed of the algorithm can be effectively improved through the joint sparse domain.

Description

technical field [0001] The invention belongs to the field of image restoration, in particular 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 technique, compressed sensing (CS) is widely used in many fields, such as radar and medical imaging, seismic imaging, image processing, etc. According to the CS theory, under the premise that the signal meets the sparsity requirement, the original signal can be reconstructed with high precision by using a sampled data volume much smaller than that required by the Nyquist-Shannon sampling law. But what is even more striking is that CS also has excellent performance in high-precision reconstruction and estimation of signals. In order to balance the reconstruction accuracy of...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06N7/00G06K9/62
CPCG06T5/001G06N7/01G06F18/2136
Inventor 郭企嘉辛志男周天李海森
Owner HARBIN ENG UNIV
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