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首页> 外文期刊>Image Processing, IET >Video denoising in three-dimensional complex wavelet domain using a doubly stochastic modelling
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Video denoising in three-dimensional complex wavelet domain using a doubly stochastic modelling

机译：使用双重随机建模的三维复小波域视频降噪

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摘要

This study presents a new video denoising method in the three-dimensional (3D) discrete complex wavelet transform (DCWT) domain. The authors assume that the coefficients have zero mean and Gaussian local distributions given the unknown variances. In practice, the locally estimated variances (LEVs) are not accurate and are simply maximum-likelihood estimates from the conditional Gaussian distribution. To take into account the inaccuracies of LEVs and motivated by experiments, the authors assume that the LEVs have gamma distributions. This is equivalent to the unconditional heavy-tailed local Bessel K-form prior densities given LEVs. This model is able to more accurately model the intrascale dependency between adjacent wavelet coefficients. The authors employ both maximum a posteriori and minimum mean-squared error MMSE estimators of the unconditional distributions, to reduce the noise in the 3D DCWT domain. The authors examine their spatially adaptive algorithm for reduction of various types of noise including additive white Gaussian noise, non-stationary noise, Poisson noise and speckle noise. The proposed method results in an impressive video enhancement without any explicit use of motion estimation. This is because, the 3D DCWT is a motion selective transform and isolates the motions and directions in its sub-bands.
机译：本研究提出了一种在三维（3D）离散复数小波变换（DCWT）域中的新视频降噪方法。作者假设系数具有零均值，并且在未知方差的情况下具有高斯局部分布。在实践中，局部估计的方差（LEV）并不准确，而仅仅是根据条件高斯分布的最大似然估计。考虑到LEV的不精确性并受实验的影响，作者认为LEV具有伽马分布。这等效于给定LEV的无条件重尾局部Bessel K型先验密度。该模型能够更准确地对相邻小波系数之间的尺度内相关性建模。作者采用无条件分布的最大后验误差和最小均方误差MMSE估计量，以减少3D DCWT域中的噪声。作者研究了他们的空间自适应算法，用于减少各种类型的噪声，包括加性高斯白噪声，非平稳噪声，泊松噪声和斑点噪声。所提出的方法在不显式使用运动估计的情况下实现了令人印象深刻的视频增强。这是因为3D DCWT是运动选择变换，并且将运动和方向隔离在其子带中。

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  • 来源
    《Image Processing, IET》 |2012年第9期|p.1262-1274|共13页
  • 作者

    Rabbani H.; Gazor S.;

  • 作者单位

    Department of Biomedical Engineering, Isfahan University of Medical Sciences, Isfahan, Iran;

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