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Fractal-wavelet self-adaption image denoising method based on multivariate statistical model

A multivariate statistical model and wavelet adaptive technology, applied in the field of video processing, can solve the problems of exponential increase in complexity, complicated operation process, long time consumption and so on.

Inactive Publication Date: 2015-07-15
GUANGXI UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

These algorithms require high memory and excessive computation, which increases the complexity of the problem exponentially
[0016] In the process of realizing the present invention, the inventor found that there are at least defects such as complicated operation process, long time-consuming and low reliability in the prior art

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  • Fractal-wavelet self-adaption image denoising method based on multivariate statistical model
  • Fractal-wavelet self-adaption image denoising method based on multivariate statistical model
  • Fractal-wavelet self-adaption image denoising method based on multivariate statistical model

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Embodiment Construction

[0073] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0074] According to an embodiment of the present invention, such as figure 1 and figure 2 As shown, a fractal wavelet adaptive image denoising method based on multivariate statistical model is provided.

[0075] In the technical solution of the present invention, each block and The corresponding wavelet coefficients are and It is a quadtree composed of horizontal, vertical and diagonal three block coefficients. In this matrix and unique subtree, any wavelet coefficient are based on as the root element. Fractal wavelet transform image coding can be done with "collage coding".

[0076] The process of "collage coding" program to generate fractal wavelet...

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Abstract

The invention discloses a fractal-wavelet self-adaption image denoising method based on a multivariate statistical model. The fractal-wavelet self-adaption image denoising method comprises the following steps: an extended GGD model, namely an MGGD model, is selected to establish the multivariate statistical model, and image denoising is completed by fractal-wavelet transform; a vector (x) of P2 closest to a vector (x) of P1 is obtained by minimizing a residual R1, and a parameter alpha and a parameter beta are self-adaptively adjusted; fractal-wavelet noiseless image coding is predicted self-adaptively for noise images by quadtree-based segmentation to realize denoising. The fractal-wavelet self-adaption image denoising method can overcome defects that in the prior art, the operation process is complex, the consumed time is long, and the reliability is low, and has the advantages that the operation process is simple, the consumed time is short, and the reliability is high.

Description

technical field [0001] The invention relates to the technical field of video processing, in particular to a fractal wavelet adaptive image denoising method based on a multivariate statistical model. Background technique [0002] Behavior recognition in football match video is the frontier research direction of artificial intelligence, and the research in this aspect has great economic significance and social value. [0003] Research status and existing problems of multi-player behavior recognition in football match videos [0004] Multi-player behavior recognition in football game video involves feature extraction, target tracking and detection, behavior representation, classifier construction and behavior recognition and other specific research contents. [0005] Research on Target Tracking and Detection Technology [0006] The target tracking and detection in the process of multi-player behavior recognition in football game video is mainly the tracking and detection of p...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 王智文刘美珍罗功坤阳树洪欧阳浩蒋联源李春贵夏冬雪
Owner GUANGXI UNIVERSITY OF TECHNOLOGY
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