[1] Wang Y D, Gao S K. New technology of ultrasonic imaging and its clinical application[J]. Beijing Biomedical Engineering, 25, 553-555, 560(2006).

[2] Chen T Y. Research on methods of medical ultrasound image denoising[D]. Harbin: Harbin Institute of Technology(2008).

[3] Pizurica A, Philips W, Lemahieu I et al. A versatile wavelet domain noise filtration technique for medical imaging[J]. IEEE Transactions on Medical Imaging, 22, 323-331(2003).

[4] Strintzis M G, Kokkinidis I. Maximum likelihood motion estimation in ultrasound image sequences[J]. IEEE Signal Processing Letters, 4, 156-157(1997). http://ieeexplore.ieee.org/document/586034

[5] Ng J, Prager R, Kingsbury N et al. Wavelet restoration of medical pulse-echo ultrasound images in an EM framework[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 54, 550-568(2007). http://europepmc.org/abstract/MED/17375824

[6] Wang L, Li D Y, Wang T F et al. Filtering of medical ultrasonic images based on a modified anistropic diffusion equation[J]. Journal of Electronics, 24, 209-213(2007).

[7] Chen T T, Shen M F, Yang J Y. Algorithm based on compound anisotropic diffusion for image denoising[J]. Journal of Data Acquisition and Processing, 29, 757-763(2014).

[8] Yu Y J, Acton S T. Speckle reducing anisotropic diffusion[J]. IEEE Transactions on Image Processing, 11, 1260-1270(2002).

[9] Shao D G, Liu P, Liu D C. Characteristic matching-based adaptive fast bilateral filter for ultrasound speckle reduction[J]. Pattern Recognition Letters, 34, 463-469(2013). http://dl.acm.org/citation.cfm?id=2442491

[10] Feng P, Li Y. Semiconductor laser parameter inverse design method based on artificial neural network and particle swarm optimization[J]. Chinese Journal of Lasers, 46, 0701001(2019).

[11] Chen D, Wang C H, Yao B Y. Modulation recognition of wireless optical subcarrier based on fuzzy clustering and back propagation neural net[J]. Laser & Optoelectronics Progress, 55, 060602(2018).

[12] Zhang Q, Li B[J]. The formation principle and model of ultrasound speckle Electronic Technology & Software Engineering, 2014, 118-120.

[13] Singh P. Mukundan R, de Ryke R. Synthetic models of ultrasound image formation for speckle noise simulation and analysis[C]//2017 International Conference on Signals and Systems (ICSigSys), May 16-18, 2017, Sa, 278-284(2017).

[14] Wang X J. The application research of speckle tracking in echocardiography processing and analysis[D]. Jinan: Shandong University(2007).

[15] Tuthill T A, Sperry R H, Parker K J. Deviations from Rayleigh statistics in ultrasonic speckle[J]. Ultrasonic Imaging, 10, 81-89(1988).

[16] Mohana Shankar P. A general statistical model for ultrasonic backscattering from tissues[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 47, 727-736(2000).

[17] Zhang H Q, Chen X Q, Tao X L. Method of ultrasound image denoising based on hybrid-order PDE[J]. Information Technology, 37, 77-80, 84(2013).

[18] Mi B H, Hong W X, Song J L et al. Myocardialischemia pre-diagnosis method based on infrared thermal imaging and BP neural network[J]. Laser & Optoelectronics Progress, 56, 011101(2019).

[19] Paris S, Kornprobst P, Tumblin J et al. Bilateral filtering: Theory and applications[M]. Hanover: Now Publishers Inc.(2008).

[20] Behar V, Adam D, Friedman Z. A new method of spatial compounding imaging[J]. Ultrasonics, 41, 377-384(2003). http://www.sciencedirect.com/science/article/pii/S0041624X03001057

[21] Chen Y, Yin R M, Flynn P et al. Aggressive region growing for speckle reduction in ultrasound images[J]. Pattern Recognition Letters, 24, 677-691(2003).