Method for removing image noise based on kernel regression total variation

A kernel regression, total variation technology, applied in the field of computer vision, can solve problems such as image distortion and false details

Inactive Publication Date: 2016-06-15
JIANGSU UNIV
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Problems solved by technology

Buades et al. (B.A.Buades and J.Morel. Anon-local algorithm for image denoising [C]. Proceedings of CVPR, 60-65, 2005.) proposed a non-local mean image denoising method based on the idea that natural images contain a large amount of and repeated structural information. Although the obtained image has rich detail information, it will bring a lot of "pseudo-details", resulting in image distortion

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  • Method for removing image noise based on kernel regression total variation
  • Method for removing image noise based on kernel regression total variation
  • Method for removing image noise based on kernel regression total variation

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Embodiment Construction

[0086] The image denoising method of the present invention will be described below with reference to the accompanying drawings. Such as figure 1 As shown, the method includes the following steps:

[0087] Step 1. Construct the kernel regression full variation regularization term to obtain the local structure information of the image. The process of constructing the full variational regularization term for kernel regression includes:

[0088] 1) Define the mathematical model of the noisy image as

[0089] the y i =z(x i )+ε i i=1,....,P,x i =[x 1i , x 2i ] T (1)

[0090] where y i is the noisy image at x i (x 1i and x 2i is the sampling point near the spatial domain coordinates), z( ) is the regression function to be estimated, ε i Represents independent and identically distributed noise with a mean of 0, and P is the number of sampling points.

[0091] 2) Expand the function locally at the point to be estimated, x is x i A sampling point nearby, then there is ...

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Abstract

The invention discloses a method for removing image noise based on kernel regression total variation, and belongs to the field of computer vision. The method comprises the steps: (1), enabling a kernel regression method to be extended in a bounded total variation space, and constructing a regularization item comprising noise image local priori knowledge; (2), constructing a regularization item of non-local priori knowledge through employing non-local similar information in an image, and building an image noise reduction model; (3), carrying out the quick solving of the constructed model through employing a split Bregman method, and obtaining a preliminary estimated image; (4), carrying out the residual error iteration optimization of the preliminary estimated image, and achieving the optimal image noise removing effect. The method keeps the detail and texture information of the image well while maintaining an image structure.

Description

technical field [0001] The invention belongs to the technical field of computer vision, in particular to an image denoising method based on kernel regression total variation. Background technique [0002] Various noises inevitably exist in the process of image acquisition and transmission. Effectively removing noise is a very important and critical step in image processing applications. In recent years, image processing methods based on total variation have better "edge-preserving" characteristics, and have been applied to image denoising, image enhancement, etc. S.Osher et al. (S.OsherL.I.RudinandE.Fatemi.Nonlineartotalvariationbasednoiseremovalalgorithms[J].ProceedingsofCVPR, 60:259-268, 1992.) proposed to use full variation for image denoising, but due to the image denoising based on full variation Denoising methods over-smooth the details of the image, which limits its application. With the development of machine learning theory, non-parametric methods have been widel...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T5/002
Inventor 李林魏新华
Owner JIANGSU UNIV
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