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Non-local mean image de-noising method with noise intensity self-adaptation function

A non-local mean, noise intensity technology, applied in image enhancement, image data processing, instruments, etc., can solve problems such as unsatisfactory image denoising effect

Inactive Publication Date: 2015-05-13
XIAN UNIV OF TECH
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Problems solved by technology

[0005] The purpose of the present invention is to provide a noise intensity self-adaptive non-local mean image denoising method to solve the technical problem that the existing non-local mean denoising method uses the same denoising intensity parameter to cause unsatisfactory image denoising effect

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Embodiment Construction

[0038] The present invention will be described in detail below with reference to the drawings and specific embodiments.

[0039] The invention provides a noise intensity self-adaptive non-local mean image denoising method, which is specifically implemented according to the following steps:

[0040] Step 1: Collect the grayscale bar image on the KODAK Gray Scale grayscale card and input it into the computer. The grayscale bar image contains 20 areas with different brightness from black to white. The grayscale bar image is recorded as z( i), where i represents a pixel point, z represents the brightness value of the pixel point, and the brightness area is recorded as X m , 1≤m≤20;

[0041] Step 2: Obtain the optimal denoising intensity parameters under different brightness, the specific process is as follows:

[0042] Step 2.1, use the non-local mean algorithm to denoise the grayscale image z(i) using different denoising intensity parameters to obtain different brightness Y m ...

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Abstract

The invention discloses a non-local mean image de-noising method with a noise intensity self-adaptation function. The non-local mean image de-noising method with the noise intensity self-adaptation function comprises the steps that firstly, a gray scale strip image is collected, and de-noising processing is conducted on the gray scale strip image by using different de-noising intensity parameters through a non-local mean method, so that optimal de-noising intensity parameters under the condition of different degrees of brightness are obtained; then, optimal de-noising intensity parameters corresponding to other degrees of brightness are calculated through a linear interpolation method; finally, de-noising processing is conducted on the image in a logarithm domain through the optimal de-noising intensity parameters corresponding to different degrees of brightness, and exponential transformation is conducted on the de-noised image in the logarithm domain, so that a final de-noised image is obtained. The non-local mean image de-noising method overcomes the defect that in an existing method, de-noising intensity parameters are fixed, and the de-noising effect of the image is improved; due to processing in the logarithm domain, the difference of brightness of pixels in a dark region can be increased, the difference of brightness of pixels in a bright region can be reduced, and the de-noising effect of the image can be improved.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and relates to a noise intensity self-adaptive non-local mean image denoising method. Background technique [0002] In the process of digital image acquisition, it will inevitably be interfered by various noise signals, which will degrade the image quality and affect the later image feature extraction, target segmentation and target recognition. Therefore, image denoising has important practical application value. [0003] Image denoising methods can be divided into two categories: spatial domain-based methods and transform domain-based methods. Methods based on the spatial domain include bilateral filtering and Gaussian filtering based on the gray similarity of a single pixel, and methods based on the transform domain such as various image denoising methods based on wavelet transform. The traditional spatial domain denoising method is based on single pixel information, which ca...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 张二虎李敬朱仁兵张卓敏
Owner XIAN UNIV OF TECH
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