Abstract：For the problem that the existing methods for noise type recognition and intensity estimation all focus on single noises, and cannot estimate the intensity of source noises in the mixed noises, a K-Nearest Neighbors (KNN) algorithm with distance threshold was proposed to recognize the single and mixed noises, and estimate the intensity of source noises in the mixed noises by combining the recognition results of mixed noises and the reconstruction of noise bases. Firstly, the data distribution in frequency domain was used as feature vector. Then the signals were identified by the noise type recognition algorithm, and the frequency domain cosine distance between reconstructed noise and real noise was adopted as the optimal evaluation criterion in the process of reconstruction of noise bases. Finally, the intensity of source noises was estimated. The experimental results on two test databases indicate that, the proposed algorithm has the average accuracy of noise type identification as high as 98.135%, and the error rate of intensity estimation of mixed noise of 20.96%. The results verify the accuracy and generalization of noise type recognition algorithm as well as the feasibility of mixed noise intensity estimation algorithm, and this method provides a new idea for the mixed noise intensity estimation. The information of mixed noise type and intensity obtained by this method contributes to the determination of denoising methods and parameters, and improves the denoising efficiency.
[1] WEN Y, NG M K, HUANG Y. Efficient total variation minimization methods for color image restoration[J]. IEEE Transactions on Image Processing, 2008, 17(11):2081-2088. [2] TANG Q. JIAO L. Image denoising with geometrical thresholds[J]. Electronics Letters, 2009, 45(8):405-406. [3] PORTILLA J, STRELA V, WAINWRIGHT M J, et al. Image denoising using scale mixtures of Gaussians in the wavelet domain[J]. IEEE Transactions on Image Processing, 2003, 12(11):1338-1351. [4] UZKENT B, BARKANA B D, YANG J. Automatic environmental noise source classification model using fuzzy logic[J]. Expert Systems with Applications, 2011, 38(7):8751-8755. [5] AKBACAK M, HANSEN J H L. Environmental sniffing:noise knowledge estimation for robust speech systems[J]. IEEE Transactions on Audio, Speech and Language Processing, 2007, 15(2):465-477. [6] CHOI J H, CHANG J H. On using acoustic environment classification for statistical model-based speech enhancement[J]. Speech Communication, 2012, 54(3):477-490. [7] KARIBASAPPA K G, HIREMATH S, KARIBASAPPA K. Neural network based noise identification in digital image[J]. International Journal on Network Security, 2016, 1:28-31. [8] KHAW H Y, SOON F C, CHUAH J H, et al. Image noise types recognition using convolutional neural network with principal components analysis[J]. IET Image Processing, 2017, 11(12):1238-1245. [9] KUMAR S V, NAGARAJU C. FFBF:cluster-based Fuzzy Firefly Bayes Filter for noise identification and removal from grayscale images[J]. Cluster Computing, 2019, 22(S1):1289-1311. [10] BALABANOVA I, GEORGIEV G, KOSTADINOVA S. Artificial neural network for identification of signals with superposed noises[C]//Proceedings of the 2017 International Conference on Intelligent Information Technologies for Industry, AISC 679. Cham:Springer, 2017:488-495. [11] CAO J, CAO M, WANG J, et al. Urban noise recognition with convolutional neural network[J]. Multimedia Tools and Applications, 2019, 78(20):29021-29041. [12] EAMDEELERD C, SONGWATANA K. Audio noise classification using bark scale features and K-NN technique[C]//Proceedings of the 2008 International Symposium on Communications and Information Technologies. Piscataway:IEEE, 2008:131-134. [13] 袁文浩,林家骏,陈宁,等. 一种基于Bark域能量分布的噪声分类方法[J]. 华东理工大学学报(自然科学版), 2013, 39(4):472-476. (YUAN W H, LIN J J, CHEN N, et al. A noise classification method based on noise energy distribution in Bark domain[J]. Journal of East China University of Science and Technology (Natural Science Edition), 2013, 39(4):472-476.) [14] YUAN W, XIA B. A speech enhancement approach based on noise classification[J]. Applied Acoustics, 2015, 96:11-19. [15] 王海超,王春光,宗哲英,等. 基于噪声类型及强度估计的狭叶锦鸡儿叶切片图像盲去噪[J]. 农业工程学报, 2017, 33(10):229-238. (WANG H C, WANG C G, ZONG Z Y, et al. Blind image denoising of microscopic slices image of Caragana stenophylla Pojark based on noise type and intensity estimation[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(10):229-238.) [16] 柳薇. SVD域的图像高斯噪声强度估计[J].中国图象图形学报, 2012, 17(8):923-933. (LIU W. Gaussian noise level estimation in SVD domain for images[J]. Journal of Image and Graphics, 2012, 17(8):923-933.) [17] 乔双,吴晓阳,赵辰一,等. 基于PCA和BM3D的噪声估计方法及其在中子图像去噪中的应用[J]. 原子能科学技术, 2018, 52(4):729-736. (QIAO S, WU X Y, ZHAO C Y, et al. Noise level estimation method based on PCA and BM3D for neutron image denoising[J]. Atomic Energy Science and Technology, 2018, 52(4):729-736.) [18] 吴疆,尤飞,蒋平. 基于回归分析和主成分分析的噪声方差估计方法[J]. 电子与信息学报, 2018, 40(5):1195-1201. (WU J, YOU F, JIANG P. Noise variance estimation method based on regression analysis and principal component analysis[J]. Journal of Electronics and Information Technology, 2018, 40(5):1195-1201.) [19] 张旗,梁德群,樊鑫,等. 基于小波域的图像噪声类型识别与估计[J].红外与毫米波学报, 2004, 23(4):281-285. (ZHANG Q, LIANG D Q, FAN X, et al. Identifying of noise types and estimating of noise level for a noisy image in the wavelet domain[J]. Journal of Infrared and Millimeter Waves, 2004, 23(4):281-285.) [20] COHEN I, BERDUGO B. Noise estimation by minima controlled recursive averaging for robust speech enhancement[J]. IEEE Signal Processing Letters, 2002, 9(1):12-15. [21] 胡岸,高勇. 基于变窗长搜索的改进型噪声估计算法[J]. 计算机系统应用, 2018, 27(9):124-129. (HU A, GAO Y. Improved noise estimation algorithm based on searching by variable window[J]. Computer Systems and Applications, 2018, 27(9):124-129.) [22] 马俊,姜卫平,邓连生,等. GPS坐标时间序列噪声估计及相关性分析[J]. 武汉大学学报(信息科学版), 2018, 43(10):1451-1457. (MA J, JIANG W P, DENG L S, et al. Estimation method and correlation analysis for noise in GPS coordinate time series[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10):1451-1457.) [23] 李航. 统计学习方法[M]. 北京:清华大学出版社, 2012:37. (LI H. Statistical Learning Method[M]. Beijing:Tsinghua University Press, 2012:37.) [24] PARK H K, LUHMANN N C Jr, DONNÉ A J H, et al. Comparative study of sawtooth physics and Alfvén waves via 2-D ECE Imaging on KSTAR, DIII-D, AUG and TEXTOR[J]. Revista Chilena de Nutricion, 2012, 37(4):439-445. [25] HAN X, LIU X, LIU Y, et al. Design and characterization of a 32-channel heterodyne radiometer for electron cyclotron emission measurements on experimental advanced superconducting tokomak[J]. Review of Scientific Instruments, 2014, 85(7):No.073506. [26] DENG B H, BROWER D L, CIMA G, et al. Imaging of core electron temperature fluctuations of tokomak plasmas[J]. IEEE Transactions on Plasma Science, 2002, 30(1):72-73.
2020, Vol. 40 
