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Image denoising method based on adaptive shear wave

A shear wave and self-adaptive technology, applied in the field of image processing, can solve the problems of unreachable peak signal-to-noise, poor application pertinence and poor adaptability, etc.

Active Publication Date: 2010-09-29
XIDIAN UNIV
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Problems solved by technology

However, because the shape of the original shear wave cannot be changed, the application of threshold denoising is poor in pertinence and adaptability, resulting in the peak signal-to-noise ratio (PSNR) of the denoising result not being able to achieve a more ideal index.

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  • Image denoising method based on adaptive shear wave
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  • Image denoising method based on adaptive shear wave

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Embodiment Construction

[0041] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0042] Step 1: Carry out the Laplacian pyramid decomposition of down-sampling on the noisy image twice, and obtain the outermost layer D2 of the detail image, the second-outer layer detail image D1, and the approximate image A.

[0043] Step 2, construct adaptive shear wave

[0044] 1. Construct existing shear wave basis functions in the frequency domain The construction steps are as follows:

[0045] 1a) Construct the lump function h 1 , the h 1 It must be differentiable from 0 to infinite in the (-2, 2) interval, and 0≤h in (-2, 2) 1 ≤1, h within [-1, 1] 1 = 1, constructed h 1 The expression is as follows:

[0046] h 1 ( ξ ) = e - 28 ( ξ ...

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Abstract

The invention discloses a threshold denoising method based on adaptive shear wave, mainly aiming to solve the problems of the existing threshold denoising method of shear wave that the direction of shear wave is not flexible and the adaptivity is poor. The method comprises the following steps: decomposing the Laplacian pyramid of down 2 sample of an image to obtain a detail image and an approximate image, constructing adaptive shear wave, using adaptive shear wave to perform direction dividing to the approximate image and obtain the direction coefficient matrix of each detail layer, performing hard threshold denoising to each coefficient matrix; then reconstructing each layer for each denoised coefficient matrix to obtain the reconstructed detail image; finally combining the detail image with the original approximate image, performing Laplacian pyramid reconstruction of down 2 sample, and finally obtaining the denoised image. The threshold denoising method of the invention is an extension for the existing shear wave; and by using the method, all the properties of the original shear wave can be completely maintained and image analysis can obtain more ideal effect.

Description

technical field [0001] The invention belongs to the field of image processing and relates to an image denoising method, which can be used for image denoising based on total variation, hard threshold and non-local mean. Background technique [0002] Shearlet transform is a "true" dimensional image representation method proposed by K.Guo and D.Labate in 2005. This method can well capture the geometric structure of the image. Shear waves provide a flexible, multi-scale, local, and directional analysis method. [0003] Shear wave analysis is a new multi-scale geometric analysis tool that inherits the advantages of curve waves and square waves. It generates shear wave functions with different characteristics through radial transformations such as scaling, shearing, and translation of basis functions. For C 2 Singular curves or curved high-dimensional signals have optimal properties. For two-dimensional signals, it can not only detect all singular points, but also adaptively tra...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 侯彪焦李成李彦涛王爽刘芳尚荣华
Owner XIDIAN UNIV
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