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Image de-noising method by combining bidimensional Hilbert transformation with BEMD (bidimensional empirical mode decomposition)

An image and original image technology, applied in the field of image denoising combined with two-dimensional Hilbert transform and BEMD, can solve the problem that the extreme point and envelope surface smoothness cannot be accurately obtained, the denoising effect is poor, and the The interpolation method does not have a unified conclusion, etc., to achieve good local time-frequency characteristics, improve efficiency and accuracy, and remove noise.

Active Publication Date: 2013-04-03
NORTH CHINA UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

[0012] The denoising method that combines two-dimensional empirical mode decomposition (BEMD) with other denoising methods is used above. Since the two-dimensional empirical mode decomposition is used for image decomposition, there is no unified conclusion on the acquisition and interpolation methods of extreme points, which cannot be accurate. Accurately obtain the extreme point and the smoothness of the envelope surface, resulting in poor denoising effect

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  • Image de-noising method by combining bidimensional Hilbert transformation with BEMD (bidimensional empirical mode decomposition)
  • Image de-noising method by combining bidimensional Hilbert transformation with BEMD (bidimensional empirical mode decomposition)
  • Image de-noising method by combining bidimensional Hilbert transformation with BEMD (bidimensional empirical mode decomposition)

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[0044] Attached below Figure 1~6 The steps of the present invention are further described in detail.

[0045] Step 1: Perform BEMD on the noisy image to get IMF (Intrinsic Mode Function) and R (Margin).

[0046] The specific implementation process of this step is divided into the following steps:

[0047] 1) Use the neighborhood method to find the local extremum points of the given original surface Iori, including all local maxima and minima.

[0048] 2) Difference calculation maximum value envelope surface E max and the minimum envelope surface E min , the two surface data

[0049] Calculate the average value to obtain the mean envelope surface data E mean .

[0050] In the process of image extremum point interpolation, there are boundary effects. In order to effectively eliminate the boundary effects, the method of mirror extension can be used to eliminate the boundary effects.

[0051] 3) Subtract the mean envelope surface from the original surface. It is then judged...

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Abstract

The invention discloses an image de-noising method by combining bidimensional Hilbert transformation with BEMD (bidimensional empirical mode decomposition). The image de-noising method includes steps of performing BEMD for an image containing noise to obtain an IMF (intrinsic mode function) and R (residue); enabling IMF components to be subjected to the bidimensional Hilbert transformation; and reconstructing images and residues which are obtained after the Hilbert transformation to obtain a de-noise image. The image de-noising method has the advantages that the image can be decomposed by means of BEMD in a multi-scale manner, a local time-frequency characteristic is realized, problems caused by one-scale de-noising by the traditional filter method are solved, and the image decomposition efficiency and efficiency are improved; and the bidimensional Hilbert transformation can realize excellent filter effects for multiplicative noise, Gaussian noise and salt-and-pepper noise of images, influence of the noise in the images to details and critical contents of the images is weakened, and the noise can be effectively removed.

Description

technical field [0001] The invention relates to an image processing technology, in particular to an image denoising method combining two-dimensional Hilbert transform and BEMD. Background technique [0002] Image signals are often disturbed by various noises during the process of generation, transmission and recording. Generally speaking, images in reality are noisy images. Usually in image processing work, before edge detection, image segmentation, feature extraction, pattern recognition and other high-level processing, it is very important to select an appropriate method for denoising. [0003] As a means of information acquisition, Synthetic Aperture Radar (SAR) has prominent strategic significance in national defense and environment. SAR images have the characteristics of all-weather and all-time, and have been widely used. [0004] SAR image speckle noise is essentially different from the noise encountered in digital image processing. The noise encountered in the proc...

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Application Information

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IPC IPC(8): G06T5/00
Inventor 张永梅季艳李强王世伟王小虎马兰
Owner NORTH CHINA UNIVERSITY OF TECHNOLOGY
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