Lunette local Wiener filtering method based on second generation Curvelet transformation

An arc-shaped window and arc-shaped technology, which is applied in image data processing, instruments, calculations, etc., can solve problems such as coefficient strangling, affecting image quality, and blurring image edges

Active Publication Date: 2010-12-29
XIDIAN UNIV
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Problems solved by technology

The hard threshold denoising method based on Curvelet transform proposed by Candes and Donoho et al. proves this point, but the biggest shortcoming of this method lies in the "over-killing" of the coefficients, that is, as long as the coefficients are smaller than the threshold, they are all regarded as Noise processing, set to zero, which will cause blurred edges of the image and affect image quality

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  • Lunette local Wiener filtering method based on second generation Curvelet transformation
  • Lunette local Wiener filtering method based on second generation Curvelet transformation
  • Lunette local Wiener filtering method based on second generation Curvelet transformation

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Embodiment Construction

[0039] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0040] Step 1: Select the test object and add Gaussian noise to obtain a noise image. The mathematical expression of the noise image is:

[0041] f=g+n

[0042]where g={g(i, j)|i, j=1, 2,...N} represents the original image, n={n(i, j)|i, j=1, 2,...N } means zero mean and variance equal to σ 2 Gaussian noise of , the noisy image is recorded as f={f(i, j)|i, j=1, 2,...N}, and N represents the size of the image.

[0043] Step 2: Carry out the second-generation Curvelet transformation on the noise image to be processed, and obtain the Curvelet coefficient at the kth sub-band position (i, j) of the noisy image in the l layer:

[0044] C(l,k,i,j)=s(l,k,i,j)+ε(l,k,i,j)

[0045] where s(l, k, i, j) and ε(l, k, i, j) are the Curvelet coefficients of the original image and noise at the kth sub-band position (i, j) of layer l, respectively.

[0046] Step 3: Perform noise variance ...

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Abstract

The invention discloses a local Wiener filtering method of an arc window based on the second generation Curvelet transform, relates to the digital image processing field, and mainly overcomes the disadvantages of 'over shrink' of coefficients caused by a threshold method based on the second generation Curvelet transform and poor directional selectivity of a square window. The method is realized by the following steps: (1) selecting a test object and adding Gaussian noise to the test object to obtain a noise image; (2) performing the second generation Curvelet transform on the noise image; (3)estimating noise variance of various subbands in a transform domain; (4) selecting an arc area with the coefficients to be processed presently as a center from various transformed subbands; (5) estimating signal variance of the present coefficients; (6) performing the local Wiener filtering on each of the coefficients; and (7) performing the second generation inverse Curvelet transform on the processed coefficients to obtain the estimation of an original image. The method has the advantages of flexible coefficient shrink, good directional selectivity, clear edge information and detail information and high peak signal-to-noise ratio, and can be used for filtering off the Gaussian noise in natural images.

Description

technical field [0001] The invention belongs to the field of digital image processing, in particular to an arc-window Wiener filtering method based on the second-generation Curvelet transform, which can be used to filter out noise in polluted natural images. Background technique [0002] During the process of image generation and transmission, images are often degraded due to the interference and influence of various noises, which will have an adverse effect on subsequent image processing, such as segmentation and compression. Therefore, before further analysis of the image, it is necessary to filter out the noise. The main goal of image denoising is to reduce the noise level while protecting the characteristics of the image, and people have developed a variety of denoising methods according to the characteristics of the actual image, the statistical characteristics of noise and the law of frequency spectrum distribution, among which the most The intuitive method is based o...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
Inventor 侯彪曹芳菊王爽焦李成张向荣马文萍
Owner XIDIAN UNIV
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