Adaptive wavelet threshold function image noise suppression method

A wavelet threshold and image noise technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problems of edge oscillation, constant deviation, over smoothing, etc., to improve error increase, reduce deviation, mean square error Improved effect

A wavelet threshold and image noise technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problems of edge oscillation, constant deviation, over smoothing, etc., to improve error increase, reduce deviation, mean square error Improved effect

CN106570843AInactive Publication Date: 2017-04-19SHANDONG UNIV OF TECH
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  • Adaptive wavelet threshold function image noise suppression method
  • Adaptive wavelet threshold function image noise suppression method
  • Adaptive wavelet threshold function image noise suppression method

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Embodiment Construction

[0023] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0024] The invention relates to an image noise suppression method of an adaptive wavelet threshold function, such as figure 1 As shown, it can be roughly divided into the following steps:

[0025] Step 1. Import an optional grayscale image containing Gaussian noise;

[0026] Step 2, carry out wavelet decomposition to noise image, select suitable wavelet and determine the level of decomposition, then carry out wavelet transformation, obtain a group of wavelet coefficients;

[0027] Step 3, performing threshold quantization processing on the wavelet high-frequency coefficients after wavelet decomposition to obtain a set of threshold-processed wavelet coefficients;

[0028] Step 4: Reconstruct the thresholded wavelet coefficients to obtain a noise-suppressed image.

[0029] The present invention firstly sets a threshold, and for the wavelet coe...

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Abstract

The present invention discloses an adaptive wavelet threshold function image noise suppression method, and belongs to the gray image noise processing technology field. The method of the present invention comprises the following realization steps of 1 importing and optionally selecting a gray image containing the Gauss noise; 2 carrying out the wavelet decomposition on a noise image, and selecting an appropriate wavelet and determining the decomposition level, and then carrying out the wavelet transform to obtain a set of wavelet coefficients; 3 carrying out the threshold quantification processing on the wavelet high frequency coefficients after the wavelet decomposition to obtain a set of wavelet coefficients after the threshold processing; 4 reconstructing the wavelet coefficients after the threshold processing, thereby obtaining a noise suppression image. The method of the present invention solves the disadvantage that a hard threshold function is not continuous at a threshold, at the same time, enables the disadvantage that an error of a soft threshold function estimation coefficient is increased, to be improved correspondingly, and enables the image peak signal to noise ratio to be improved effectively, an image mean square error to be reduced and an image restoration effect to be improved better.

Description

technical field [0001] The invention relates to the technical field of grayscale image noise processing, in particular to an adaptive wavelet threshold function image noise suppression method. Background technique [0002] Images are often polluted by various noises in the process of acquisition or transmission, which will affect subsequent processing processes such as image segmentation and recognition. Wavelet transform is one of the effective methods in signal processing at present. Because it has good localization properties in both time domain and frequency domain, it can analyze the signal components in the specified frequency band and time period, so it is used in image processing. Has a wide range of applications. [0003] In the wavelet threshold denoising technology, the most critical thing is how to select the threshold and how to quantify the threshold. To some extent, it is directly related to the quality of signal denoising. At present, the classic representa...

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Application Information

Patent Timeline
19 Apr 2017
Publication
CN106570843A
IPC
G06T5/00
CPC
G06T2207/20192; G06T2207/10004; G06T5/70
Inventors
李东兴; 张起