A Layered Adaptive Threshold Video Denoising Method

An adaptive threshold and video technology, applied in the field of video denoising, can solve problems such as not being able to make good use of Surfacelet, high hardware, not making good use of transform domain coefficients, and direction information.

Active Publication Date: 2016-09-07
HOHAI UNIV
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Problems solved by technology

[0006] However, the existing video denoising algorithm based on Surfacelet transform cannot make good use of the advantages of Surfacelet transform, and does not make good use of the field and direction information of the coefficients in the transform domain.
Moreover, Surfacelet transformation has higher requirements on hardware, which also limits its application.

Method used

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  • A Layered Adaptive Threshold Video Denoising Method
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Embodiment Construction

[0060] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0061] A hierarchical adaptive threshold video denoising method, such as figure 1 shown, including the following steps:

[0062] Step 1, input the noisy video, and perform Surfacelet transformation on the noisy video, and decompose it into 4 layers;

[0063] Step 2, use the median estimation method to estimate the standard deviation of the highest-layer directional subband noise in the Surfacelet transform domain:

[0064] σ 1 =median(|y(i,j,k)|) / 0.6745

[0065] In the formula, y(i,j,k) is a certain direction subband of the highest layer Surfacelet transform domain; i∈{1,2,3,…,I}, j∈{1,2,3,…,J} , k∈{1,2,3,…,K}; I, J, K are the length, width, and height of the subband, respectively;

[0066] Apply the Monte Carlo algorithm to estimate the standard deviation relationship of each layer of Gaussian white noise after Surfacelet transformation:

[...

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Abstract

The invention discloses a layered self-adaptive threshold video denoising method. The method comprises the following steps: inputting a video with noise, and performing Surfacelet transformation on the video; respectively performing noise variance estimating on a coefficient decomposed by each Surfacelet layer in a direction sub-band; utilizing the size of the sub-band coefficient of the direction of each layer to calculate an initial threshold; for a high-layer decomposition sub-band, utilizing coefficient field information to perform self-adjustment on the obtained initial threshold of the layer; for other layers, utilizing a direction-energy ratio to perform self-adjustment on the initial threshold of each layer; and utilizing a soft threshold function to perform denoising processing, and reconstructing a denoised coefficient to obtain a denoised video. The method provided by the invention remarkably reduces calculating complexity, improves the PSNR value of the denoised video, can effectively keep the detail information of the video, and can be applied to natural video denoising and three-dimensional image denoising.

Description

technical field [0001] The invention relates to a video denoising method, in particular to a layered adaptive threshold video denoising method. 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. Traditional video denoising methods are divided into space domain, time domain and transform domain. Spatial filtering includes filtering methods such as median f...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N19/64H04N19/166
Inventor 鹿浩王佳希陈亮曹宁
Owner HOHAI UNIV
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