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摘要: 噪声估计在视频去噪领域具有重要的研究意义.实际生活中的噪声都是未知的，然而现存的视频去噪算法通常都假定视频的噪声水平是已知的，本文提出一种基于主成分分析（Principal component analysis，PCA）的分块视频噪声估计算法.首先，基于帧间进行块匹配寻找相似块，得到差分图像以消除视频运动的影响；其次，将正态分布函数作为阈值函数简化噪声估计算法模型；最后，设置明确迭代指标使得估计的结果更加精确，且降低了计算复杂度.主观视觉效果和客观指标对比表明，本文提出的基于主成分分析的分块视频噪声估计算法比其他优秀的噪声估计算法误差小同时鲁棒性高，能准确地估计视频噪声.Abstract: Noise estimation is an important issue in video denoising applications. However, in practice the noise level is unknown in most cases, but most existing denoising algorithms simply assume the noise level is known beforehand. In this paper, we propose a block-based video noise estimation algorithm via the principal component analysis (PCA). Firstly, similar blocks are searched by block matching between frames, and the difference image is obtained to eliminate the influence of video motion. Secondly, a thresholding function of normal distribution is used to simplify the model of noise estimation. Finally, setting clear iterative metrics makes the estimation results more accurate and reduces the computational complexity. Subjective and objective comparisons show that, compared with other state-of-art algorithms, the noise estimation of the proposed video denoising algorithm is robust against small errors and achieves outstanding denoising effect.1) 本文责任编委 桑农
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图 4 本文算法迭代噪声水平估计流程图
Fig. 4 Flowchart of the iterative noise level estimation for proposed algorithm
图 5 加噪20 dB视频序列(Flower, Football)估计误差
Fig. 5 Noise estimation error for 20 dB noisy sequences (Flower, Football)
图 6 加噪30 dB视频序列(Flower, Football)估计误差
Fig. 6 Noise estimation error for 30 dB noisy sequences (Flower, Football)
图 7 加噪40 dB视频序列(Flower, Football)估计误差
Fig. 7 Noise estimation error for 40 dB noisy sequences (Flower, Football)
表 1 不同分布函数噪声估计对比
Table 1 Comparison of noise estimation for different function
Noise level (dB) Lena Akiyo Bus Coastguard ${{\sigma }_{n}}=10$ Liu等[14] 9.68 9.88 9.69 9.79 ${{\sigma }_{n}}=10$ Proposed 9.86 9.79 9.97 9.93 ${{\sigma }_{n}}=20$ Liu等[14] 19.56 19.61 19.64 19.67 ${{\sigma }_{n}}=20$ Proposed 19.72 19.65 19.78 19.74 ${{\sigma }_{n}}=30$ Liu等[14] 29.54 29.54 29.13 29.59 ${{\sigma }_{n}}=30$ Proposed 29.65 29.81 29.50 29.67 ${{\sigma }_{n}}=40$ Liu等[14] 38.43 39.34 38.99 39.37 ${{\sigma }_{n}}=40$ Proposed 39.50 39.38 39.64 39.60 
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表 2 VBM3D、PID、VBM4D和本文算法的PSNR和SSIM对比
Table 2 The comparisons of PSNR and SSIM results of VBM3D, PID, VBM4D and proposed algorithm
Noise level (dB) Algorithm Akiyo PSNR/SSIM Mobile PSNR/SSIM Flowergarden PSNR/SSIM Foreman PSNR/SSIM Football PSNR/SSIM ${{\sigma }_{n}}=10$ VBM3D 35.488/0.877 32.374/0.954 34.250/0.984 34.313/0.902 33.048/0.951 ${{\sigma }_{n}}=10$ PID 31.396/0.763 29.538/0.917 31.276/0.962 31.094/0.844 30.048/0.921 ${{\sigma }_{n}}=10$ VBM4D 30.290/0.730 29.727/0.915 29.972/0.842 30.078/0.813 29.833/0.923 ${{\sigma }_{n}}=10$ Proposed 37.842/0.944 32.454/0.972 33.675/0.983 36.295/0.938 33.488/0.960 ${{\sigma }_{n}}=20$ VBM3D 30.239 /0.715 27.653/0.897 29.444/0.961 29.587/0.786 28.171/0.868 ${{\sigma }_{n}}=20$ PID 26.011/0.836 24.572/0.833 25.945/0.896 25.937/0.675 24.889/0.793 ${{\sigma }_{n}}=20$ VBM4D 29.594/0.767 27.249/0.898 27.599/0.852 28.904/0.797 27.727/0.885 ${{\sigma }_{n}}=20$ Proposed 34.986/0.925 28.322/0.936 29.386/0.957 33.098/0.878 29.890/0.894 ${{\sigma }_{n}}=30$ VBM3D 27.463/0.609 25.025/0.853 26.611/0.932 26.934/0.698 25.406/0.783 ${{\sigma }_{n}}=30$ PID 23.051/0.815 21.803/0.769 22.895/0.825 23.017/0.565 21.929/0.676 ${{\sigma }_{n}}=30$ VBM4D 29.026/0.780 25.884/0.883 26.185/0.848 28.208/0.782 26.557/0.842 ${{\sigma }_{n}}=30$ Proposed 32.579/0.866 26.045/0.898 27.098/0.932 31.379/0.835 27.995/0.825
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