Surfacelet domain BKF model Bayes video denoising method

A Bayesian and video technology, applied in the Surfacelet domain BKF model Bayesian video denoising, video image additive noise removal field, can solve the problem of insufficient use of coefficient relationship, the denoising effect needs to be improved, etc., to overcome The effect of underutilization, denoising hold, improving denoising effect

Inactive Publication Date: 2014-04-02
XIDIAN UNIV
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Problems solved by technology

Although this method can improve the effect of video denoising by using the neighborhood relationship of the coefficients, effectively maintain the detailed information of the video, and achieve a good video denoising effect, but there is still a shortcoming that this method only uses a small number of neighbors of the coefficients. The domain information does not make full use of the coefficient relationship outside the neighborhood, and the denoising effect needs to be improved

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  • Surfacelet domain BKF model Bayes video denoising method
  • Surfacelet domain BKF model Bayes video denoising method
  • Surfacelet domain BKF model Bayes video denoising method

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Embodiment Construction

[0052] Attached below figure 1 The present invention is further described.

[0053] Step 1, input a video to be denoised, the video size is 192×192×192 pixels, and the added noise is Gaussian white noise.

[0054] Step 2, obtain the Surfacelet domain coefficients of the video to be denoised.

[0055] Call the Surfacelet toolkit to perform Surfacelet transformation on the video to be denoised, and obtain the high-frequency subband coefficients in the Surfacelet domain of the video to be denoised.

[0056] Step 3, use the noise estimation formula to estimate the noise standard deviation of the video to be denoised.

[0057]In the high-frequency detail subband of the Surfacelet domain of the noisy video, its energy is mainly provided by the noise. The noise in the Surfacelet domain is independent and identically distributed Gaussian white noise, and the noise variance is constant, so Donoho proposes to use the robust median to Estimate the noise standard deviation.

[0058] T...

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Abstract

A Surfacelet domain BKF model Bayes video denoising method comprises the following steps: inputting a video to be denoised; acquiring a high frequency subband coefficient; evaluating a noise standard deviation; acquiring a Surfacelet domain high frequency subband BKF distribution shape parameter and a size parameter of the video to be denoised; judging the value of the BKF distribution shape parameter; acquiring a Surfacelet domain coefficient of the denoised video; and acquiring the denoised video. According to the method provided by the invention, the marginal distribution of the Surfacelet coefficient of the video is modeled by using the BKF function, so that by making full use of the correlation of the Surfacelet domain high frequency subband coefficient of a video image, marginal detail information of the video image can be kept well on the premise of effectively denoising.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a Bayesian video denoising method in the Surfacelet domain BKF model in the technical field of video processing. The invention can be applied to the removal of video image additive noise. Background technique [0002] In the process of video image acquisition and transmission, the introduction of noise is inevitable. Since video images have great correlation between adjacent pixels and frames, while noise is random and irrelevant, this provides a theoretical basis for video image noise removal in the spatio-temporal domain. Since the noisy video image has been transformed by wavelet transform and Surfacelet, the video image and noise have different characteristics in the transform domain, and the coefficients in the transform domain have a certain distribution law. Denoising according to the coefficient characteristics and distribution rules of the change domain c...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N5/21G06T5/00
Inventor 田小林焦李成聂继勇张小华缑水平马文萍钟桦朱虎明
Owner XIDIAN UNIV
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