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Fractal-wavelet self-adaptive image denoising method based on multivariate statistic model

A multivariate statistical model and wavelet adaptive technology, applied in the field of image processing, can solve problems such as instability and pathology

Inactive Publication Date: 2013-02-20
GUANGXI UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

Although the P-M method has achieved certain results in suppressing noise and preserving important features of the image, it is pathological and unstable.

Method used

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  • Fractal-wavelet self-adaptive image denoising method based on multivariate statistic model
  • Fractal-wavelet self-adaptive image denoising method based on multivariate statistic model
  • Fractal-wavelet self-adaptive image denoising method based on multivariate statistic model

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Embodiment Construction

[0061] The present invention will be described in detail below in conjunction with specific embodiments.

[0062] 1 Fractal wavelet transform denoising

[0063] Fractal Wavelet Transforms (FWT) is introduced in the process of fractal image compression to reduce block effects and computational complexity [25-29] . Fractal wavelet transform operations involve scaling and copying subtrees of wavelet coefficients to lower subtrees, exactly analogous to how fractal image coders operate in the spatial domain. The essence of fractal wavelet denoising is to predict the fractal coding of the noise-free image from the noise image.

[0064] 1.1 Introduction to Fractal Wavelet Transform Image Coding

[0065] The discrete wavelet transform (DWT) coefficients of a two-dimensional signal (image) are a standard matrix array. Assuming that the two-dimensional wavelet basis functions are constructed by a suitable tensor product of the one-dimensional scaling function and the wavelet functio...

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Abstract

The invention discloses a fractal-wavelet self-adaptive image denoising method based on a multivariate statistic model. The method includes: step one, subjecting a noisy image to homomorphic transform through which an original image IB containing multiplicative noise is transformed into an image IB' only containing additive noise; step two, performing fractal-wavelet transform on a noisy signal f (k), selecting a wavelet basis and a wavelet decomposition layer j to obtain corresponding wavelet coefficients; step three, selecting an MGGD multivariate statistic model for self-adaptive solution of a parameter alpha and a parameter beta, and obtaining the most suitable parameter value alpha and beta after analysis for the distribution condition of the wavelet coefficient of a natural image; step four, for the wavelet coefficients obtained through decomposition, performing noise-free predictive coding on the noisy image by using a fractal-wavelet coding method; and step five, performing wavelet reconstruction by using the wavelet coefficients to obtain estimation signals which are image signals after denoising. Compared with other algorithms, the method has better denoising effect and high edge preserving capacity, and is particularly suitable for eliminating Gaussian-impulse mixed noise.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a fractal wavelet adaptive image denoising method based on a multivariate statistical model. Background technique [0002] Because the image signal is inevitably disturbed by noise in the process of acquisition, transmission and storage, the noise reduces the image quality, submerges the edge and detail features of the image, and brings difficulties to image analysis and subsequent processing. The elimination of image noise is an important research content in image processing. Whether the noise can be effectively filtered will directly affect the follow-up work of image processing. Before further processing such as edge detection, image segmentation, feature extraction and pattern recognition, it is a very important preprocessing step to use appropriate methods to remove noise [1] . How to effectively remove noise while maintaining the clarity of image details and imag...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 王智文刘美珍夏冬雪唐新来阳树洪罗功坤蔡启先刘智徐奕奕
Owner GUANGXI UNIVERSITY OF TECHNOLOGY
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