Method for de-noising dual-tree complex wavelet image on basis of partial differential equation

A dual-tree complex wavelet and partial differential equation technology, applied in the field of image denoising, can solve problems such as difficult to filter out impulse noise, blurred image weak edges, susceptible to noise interference, etc., to achieve suppression of pseudo-Gibbs phenomenon and The effect of the ladder effect

Inactive Publication Date: 2010-07-14
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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But this method is theoretically an ill-conditioned mathematical model, and because the diffusion coefficient function depends on the gradient value of the nearest neighb

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  • Method for de-noising dual-tree complex wavelet image on basis of partial differential equation
  • Method for de-noising dual-tree complex wavelet image on basis of partial differential equation
  • Method for de-noising dual-tree complex wavelet image on basis of partial differential equation

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[0018] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Such as figure 1 As shown, a dual-tree complex wavelet image denoising method based on partial differential equations includes the following steps:

[0019] Step 1: Input a noisy digital image. Select a digital image (such as JPG, TIFF format) as a noisy digital image, use the function u(x, y, T) to represent the digital image, (x, y) represents the pixel space position of the noisy digital image, and T represents the number The time scale of the image, when the time scale T=0, represents the noisy digital image u(x,y,0) before processing, and when the time scale T=t, represents the denoised digital image u(x, y, t).

[0020] Step 2: Perform dual-tree complex wavelet transform decomposition on the input noisy digital image. Perform a layer of dual-tree complex wavelet transform on the noisy digital image u(x, y, 0), decompose to obtain two lo...

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Abstract

The invention relates to a method for de-noising a dual-tree complex wavelet image on the basis of partial differential equation. The method comprises the following steps: inputting a noised digital image; carrying out the dual-tree complex wavelet transform decomposition on the inputted noised digital image to obtain two low-frequency sub-band images and six high-frequency detailed sub-band images; carrying out the isotropic diffusion on the two decomposed low-frequency sub-band images; designing an improved adaptive model; calculating the dual-tree complex wavelet transform modulus and gradient modulus of the high-frequency detain sub-band images on each direction, and designing an adaptive diffusion coefficient function to improve the P-M (Perona-Malik) model (i.e., the isotropic diffusion model) by using the weighted average of the dual-tree complex wavelet transform modulus and gradient modulus; carrying out the diffusion processing on the improved adaptive model; carrying out the isotropic diffusion on the six high-frequency sub-band images; and carrying out the dual-tree complex wavelet transform, and outputting the de-noised digital image. The invention has the beneficial effect that more detailed information of the image can be preserved on the premise that the higher rate of image de-noising is maintained.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to an image denoising method in the technical field of digital image processing such as image enhancement or image restoration. Background technique [0002] Image denoising is a very important preprocessing technology for subsequent image processing such as image analysis and computer vision. Its function is to improve the signal-to-noise ratio of the image, and retain the detailed information of the image while denoising, thereby highlighting the characteristics of the image. There are four main types of existing image denoising methods, the first type is image denoising method based on filter; the second type is image denoising method based on statistical signal processing; the third type is image denoising method based on wavelet transform method. The fourth category is image denoising methods based on partial differential equations of heat conducti...

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Application Information

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IPC IPC(8): G06T5/00
Inventor 刘金华佘堃
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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