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Method for denoising space self-adaption threshold video based on Surfacelet transform domain

An adaptive threshold, transform domain technology, applied in the field of video processing

Inactive Publication Date: 2011-08-17
XIDIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The object of the present invention is to overcome the shortcoming of above-mentioned existing method, provide a kind of method that can reduce algorithm complexity and can effectively solve phenomena such as artifact, smearing and false Gibbs effect while improving video denoising effect. Video Denoising Method Based on Surfacelet Transform and Spatial Adaptive Threshold

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  • Method for denoising space self-adaption threshold video based on Surfacelet transform domain
  • Method for denoising space self-adaption threshold video based on Surfacelet transform domain
  • Method for denoising space self-adaption threshold video based on Surfacelet transform domain

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Embodiment 1

[0052] refer to figure 1 , the present invention is a kind of space adaptive threshold video denoising method based on Surfacelet transformation domain, and concrete implementation process is as follows:

[0053] Step 1. Input the noisy video, and perform Surfacelet transformation on the noisy video, decompose 4 layers, and the number of direction sub-bands in each layer is 192, 192, 48, 12 respectively;

[0054] Step 2. Estimate the standard deviation of the highest frequency layer noise in the Surfacelet transform domain using the following formula:

[0055] σ (i，j，k) =median(|y(i,j,k)|) / 0.6745

[0056] In the formula: y(i, j, k) is a certain direction sub-band of Surfacelet transformation domain;

[0057] The Monte Carlo algorithm is used to estimate the standard deviation relationship of each layer of Gaussian white noise after Surfacelet transformation, and the following formula is obtained:

[0058] σ n = σ ...

Embodiment 2

[0088] The implementation of the method for denoising video based on the spatial adaptive threshold value of the Surfacelet transform domain is the same as that in Embodiment 1.

[0089] The denoising effect of the present invention can be further illustrated by the following experiments:

[0090] 1. Experimental conditions and content

[0091] The experimental simulation environment is: MATLAB R2009b, CPU AMD Athlon×23.00GHz, memory 3.25G, Window7 Professional.

[0092] The experimental content includes: use the video sequence Mobile and Coastguard with the size of 192×192×192, and the variance of adding noise is 20, 30, 40, 50 respectively. Mobile video sequence has strong directionality and small motion range, while Coastguard video sequence contains rich detail information and large motion range. The Surfacelet transform decomposes into four layers, and the number of direction subbands in each layer is: 192, 192, 48, 12.

[0093] 2. Experimental results

[0094] With t...

Embodiment 3

[0097] The implementation of the method for denoising video based on the spatial adaptive threshold value of the Surfacelet transform domain is the same as that in Embodiment 1.

[0098] Figures (4d) and (6d) show the denoising results based on the Surfacelet transform bayes threshold algorithm. From the denoising results in the above two figures, it can be seen that the Surfacelet transform-based bayes threshold algorithm does not use the coefficient neighborhood information of the Surfacelet transform domain, see Figure ( 6d) The sailboat mast, especially the top, is not clear, and the texture details in Figure (4d) are seriously lost, and the denoising results are not ideal. Compared with the details of the above two figures, the present invention has higher resolution.

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Abstract

The invention discloses a method for denoising a space self-adaption threshold video based on a Surfacelet transform domain, and the method is mainly used for solving the phenomena of unsatisfactory video denoising effects, excessive denoising course complexity, artifact and pseudo Gibbs' effect in a solution course and the like. The method disclosed by the invention is implemented through the following courses: outputting a video to be denoised and carrying out Surfacelet transform; respectively evaluating the noise of factors in the direction subband of each Surfacelet decomposition; computing self-adaption thresholds by utilizing the space energy values of the factors; adjusting the thresholds by utilizing the adjacent domain information of the factors; denoising by utilizing a threshold function; and reconfiguring the denoised factors, thus obtaining the denoised video. Compared with the prior art, the method disclosed by the invention has the advantages of obviously reducing the computing complexity, enhancing the PSNR (Peak Signal to Noise Ratio) value of the denoised video and effectively maintaining the detail information of the video, and can be used for natural video denoising and three-dimensional image denoising.

Description

technical field [0001] The invention belongs to the field of video processing, and mainly relates to video denoising, in particular to a surfacelet transform domain-based space adaptive threshold video denoising method, which can be used for natural video denoising and three-dimensional image denoising. Background technique [0002] With the enhancement of the processing capabilities of modern computers and imaging equipment, research on the acquisition and application of high-resolution three-dimensional and higher-dimensional spatial data has been carried out in many fields, including biomedical images, video images, astronomical images outside the galaxy, computer vision, and 3D SAR images, etc. In order to efficiently analyze and represent this massive amount of data, new signal processing tools need to be created and applied in different engineering domains. [0003] The research on video denoising initially takes the image as a unit and processes it frame by frame. Tr...

Claims

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Application Information

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IPC IPC(8): H04N5/21H04N7/26
Inventor 田小林焦李成段营张小华缑水平马文萍钟桦朱虎明
Owner XIDIAN UNIV
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