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Infrared remote sensing image super-resolution reconstruction method based on TGV regularization

A technology for super-resolution reconstruction and remote sensing images, which is applied in the field of infrared remote sensing image super-resolution reconstruction based on TGV regularization, which can solve the problems of low contrast, blurred edges, and complex components, achieve good detail retention, and improve peak reliability. The effect of noise ratio

Inactive Publication Date: 2018-07-17
SHENGDA ECONOMICS TRADE & MANAGEMENT COLLEGE OF ZZ +1
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[0004] The main purpose of the present invention is to provide a method for super-resolution reconstruction of infrared remote sensing images based on TGV regularization to solve the problem of low resolution, low contrast, blurred edges, low signal-to-noise ratio, composition complicated question

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  • Infrared remote sensing image super-resolution reconstruction method based on TGV regularization
  • Infrared remote sensing image super-resolution reconstruction method based on TGV regularization
  • Infrared remote sensing image super-resolution reconstruction method based on TGV regularization

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[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0024] First, set is an open region, α=(α 0 ,α 1 ,…,α k-1 )>0, (k≥1). And, for any The k-order TGV is defined as Equation (1) as follows:

[0025]

[0026] where, i=0,1,...,k-1, Sym k (R n ) is a k-order symmetric tensor space.

[0027] When k=1, α=α 0 = 1, formula (1) can be rewritten into formula (2), as follows:

[0028]

[0029] Among them, formula (2) is the dual definition of total variation. In this sense, TGV is the promotion and extension of TV. Using the Legendre-Fenchel duality of formula (2), formula (3) can be obtained as follows:

[0030]

[0031] in, is a symmetric gradient operator.

[0032] When k=2, formula (1) can be rewritten into formula (4), as follows:

[0033]

[0034] where BD(Ω) de...

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Abstract

The invention discloses an infrared remote sensing image super-resolution reconstruction method based on TGV regularization. The method comprises the steps that a remote sensing infrared image super-resolution reconstruction model based on TGV regularization is obtained; a Huber function is utilized to perform semi-quadratic regularization to split the remote sensing infrared image super-resolution reconstruction model based on TGV regularization, so that a split image super-resolution reconstruction model is obtained; and an alternate direction multiplier method is utilized to perform value solving on the split image super-resolution reconstruction model to generate a super-resolution reconstruction image. Through the method, a noise reduction function can be achieved, good detail maintaining capability is obtained, and a peak signal-to-noise ratio, a signal-to-noise ratio and evaluation index values of an average structure similarity, information entropy, etc. can be effectively increased.

Description

technical field [0001] The invention relates to the technical field of resolution reconstruction, in particular to a method for super-resolution reconstruction of infrared remote sensing images based on TGV regularization. Background technique [0002] Image resolution is a key index for evaluating image quality. In practical problems, it is very natural to hope that the obtained image has higher resolution and better quality. Because infrared remote sensing imaging has the characteristics of passive work, strong anti-interference ability, strong target recognition ability, and all-weather work, it has been widely used in military reconnaissance, surveillance and guidance. Compared with visible light remote sensing images, infrared remote sensing images have disadvantages such as low resolution, low contrast, blurred edges, low signal-to-noise ratio, and complex components. [0003] According to the imaging principle of remote sensing CCD camera, the most direct measure to ...

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Application Information

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IPC IPC(8): G06T3/40
CPCG06T3/4053
Inventor 时文俊郭从洲童晓冲李贺张志银冯改红乔木刘广彦
Owner SHENGDA ECONOMICS TRADE & MANAGEMENT COLLEGE OF ZZ
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