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Image denoising method based on Treelet and non-local means

A non-local mean, image technology, applied in the field of image processing

Inactive Publication Date: 2011-05-18
XIDIAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The purpose of the present invention is to propose an image denoising method based on Treelet and non-local mean for the calculation of high-dimensional data in non-local denoising, so as to reduce the computational complexity and overcome the existing methods for natural image denoising. Visual blurring phenomenon to improve the visibility of the image after denoising

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  • Image denoising method based on Treelet and non-local means

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Embodiment Construction

[0036] Reference figure 1 , The implementation steps of the present invention are as follows:

[0037] Step 1. Input the noisy image and calculate its covariance matrix

[0038] For each pixel i in the input image, the neighborhood N of a 5×5 pixel size window centered on it i The pixel gray value vector within is marked as y(N i ), calculate the covariance matrix of this neighborhood

[0039] X ^ = 1 | Ω | X i A Ω ( y ( N i ) - y ‾ ) ( y ( N i ) - y ‾ ) T - - - ( 1 )

[0040] Among them, Ω is the set of all pixels in the input image, |Ω| is the total number of all pixels in the input image, Is the pixel gray-level mean vector of the input image;

[0041] Step 2. According to the covariance matrix of the sliding window Ni Calculate the scale vector Φ of the image Treelet transformation.

[0042] (2a) According to sliding window N i Covariance matrix Calculate the similarity...

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Abstract

The invention discloses an image denoising method based on Treelet and non-local means, mainly used for solving the problems that the traditional non-local means method has poor denoising effect on high-noise corrupted natural images. The method comprises the following steps of: (1) calculating the covariance matrix of an input image; (2) calculating the scaling vector of image Treelet transform on the covariance matrix; (3) picking out a sliding window from the image pixel by pixel to be multiplexed with the scaling vector to obtain a characteristic vector function; and (4) filtering the image by the characteristic vector function pixel by pixel to obtain a denoised image. The invention has the advantage of good denoising effect on the high-noise corrupted natural images, can recover the original characteristics of the images, and can be used for preprocessing the images during variation detection and target identification.

Description

Technical field [0001] The invention belongs to the field of image processing technology, relates to the denoising of natural images under high noise conditions, and can be used to carry out forest resource surveys, land use and cover change detection, environmental change assessment, disaster assessment, urban planning, national defense military situation monitoring, and medical Digital image preprocessing in imaging, astronomical imaging and other fields. Background technique [0002] The main purpose of image denoising is to solve the problem of image quality degradation caused by noise interference in actual images. Denoising can improve the image quality, increase the signal-to-noise ratio, and better reflect the information carried by the image. Therefore, image denoising technology occupies a very important position in many fields. [0003] According to the characteristics of the image and the statistical characteristics of the noise, many image denoising methods have been ...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 王桂婷焦李成盖超曹娟公茂果钟桦王爽侯彪张晓华
Owner XIDIAN UNIV
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