Single-frame image super-resolution reconstruction method based on natural image statistic sparse model

A sparse model and natural image technology, applied in image enhancement, image data processing, computing, etc., can solve problems such as poor quality of reconstructed images

Inactive Publication Date: 2012-10-24
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0003] In order to overcome the shortcomings of the existing single-frame image super-resolution reconstruction algorithm based on sparse representation, the reconstruction image quality is poor, the present invention provides a single-frame image super-resolution reconstruction method based on the statistical sparse model of natural images, which uses the statistical characteristics of natural images , the Bayesian method is used to model the image super-resolution reconstruction problem, and the minimum mean square error criterion is used to estimate the high-resolution image, and high-quality super-resolution reconstruction images can be obtained

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  • Single-frame image super-resolution reconstruction method based on natural image statistic sparse model
  • Single-frame image super-resolution reconstruction method based on natural image statistic sparse model
  • Single-frame image super-resolution reconstruction method based on natural image statistic sparse model

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Embodiment Construction

[0034] (a) Statistical modeling of the image: In this embodiment, the Markov random field is used to model the statistical characteristics of the image X:

[0035] p ( X ) = 1 Z Π c ∈ Ω f ( X c ) - - - ( 1 )

[0036] Among them, Z represents the normalization constant, Ω represents the set of all pixel positions of the image X, c ∈ Ω is the index of the image coordinate position, X c Represents the image neighborhood of the image at position c, called a cluster. f( ) is the potential function of image clusters, in this scheme, the function is defined as follows:

[0037] f ( X ...

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Abstract

The invention discloses a single-frame image super-resolution reconstruction method based on a natural image statistic sparse model. The technical problem that the quality of reconstruction images of an existing single-frame image super-resolution reconstruction algorithm based on sparse representation is poor is solved. According to the technical scheme, by the aid of statistical properties of a natural image, the modeling is performed for image super-resolution reconstruction through a Bayes method, and a high-resolution image is estimated through a minimum mean-squared error standard. The obtained high-resolution image through reconstruction is natural, the pseudo-structure number is reduced, and a clear edge structure is achieved. Compared with a background technology method, the method has the advantages that super-resolution reconstruction images with high quality are obtained, and reconstruction results are improved by 1 dB to 2 dB.

Description

technical field [0001] The invention relates to a single-frame image super-resolution reconstruction method, in particular to a single-frame image super-resolution reconstruction method based on a natural image statistical sparse model. Background technique [0002] The document "Image super resolution via sparse representation, IEEE Trans. Image Processing, Vol.19(11), pp.2861-3873, 2010" discloses a single-frame image super-resolution reconstruction algorithm based on sparse representation. The super-resolution reconstruction process of this method is estimated block by block. For each low-resolution image block, the method first solves the sparse representation coefficient of the image block with respect to a low-resolution dictionary, and then uses the sparse representation coefficient to perform high-resolution processing on a high-resolution image corresponding to the low-resolution dictionary. Reconstruction of high-rate image blocks. The estimation process estimate...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 张艳宁张海超李海森朱宇孙瑾秋
Owner NORTHWESTERN POLYTECHNICAL UNIV
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