[1] Wang Q. Radiological medical technology and medical imaging technology analysis[J]. World Latest Medicine Information, 17, 143(2017).

[2] Fang L Z, Wang H Y. Rational use of medical imaging technology in medical imaging diagnosis[J]. Image Technology, 28, 3-4, 6(2016).

[3] Dai Z, Yang Y. Ultrasound image denoising based on deep learning[J]. Information Technology, 44, 57-60(2020).

[4] Fu X W, Wang Y, Chen L et al. An image despeckling approach using quantum-inspired statistics in dual-tree complex wavelet domain[J]. Biomedical Signal Processing and Control, 18, 30-35(2015).

[5] Zhang W, Sun R H, Zhang X C. Noise removal of SAR image based on improved wavelet soft-thresholding method[J]. Remote Sensing Information, 19, 4-6(2004).

[6] Jin T, Luo B. High-speed transmission and control of CCD photoelectric measurement signal[J]. Modern Electronics Technique, 34, 142-145, 152(2011).

[7] Cao X R, Xue L Y, Lin J W et al. Retinal vessel segmentation method based on matched filtering and automatic threshold[J]. Journal of Electronic Measurement and Instrumentation, 31, 51-57(2017).

[8] Han Q L, Zhang R Q, Cham W K et al. Quadtree-based non-local Kuan’s filtering in video compression[J]. Journal of Visual Communication and Image Representation, 25, 1044-1055(2014).

[9] Yang Y F, Li Q. Application of bilateral filtering algorithm on terahertz confocal scanning image denoising[J]. Laser & Optoelectronics Progress, 52, 121101(2015).

[10] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 629-639(1990).

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

[12] 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).

[13] Wu J, Wang Y Y, Chen Y et al. Speckle reduction of ultrasound images with anisotropic diffusion based on homogeneous region automatic selection[J]. Optics and Precision Engineering, 22, 1312-1321(2014).

[14] Qin D D, Chen Z J, Yan X Q. Wavelet image denoising with multilevel thresholding function[J]. Computer Engineering, 43, 202-206(2017).

[15] Zhou Z Y. Research on image despeckling models based on diffusion equations and deep learning[D](2018).

[16] Qiao L C. SAR image target recognition method combining multi-resolution representation and complex domain CNN[J]. Laser & Optoelectronics Progress, 57, 241007(2020).

[17] Xu C A, Lü Y F, Zhang X H et al. A discriminative feature representation method based on dual attention mechanism for remote sensing image scene classification[J]. Journal of Electronics & Information Technology, 43, 683-691(2021).

[18] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

[19] Chen Y, Fan R S, Wang J X et al. High resolution image classification method combining with minimum noise fraction rotation and convolution neural network[J]. Laser & Optoelectronics Progress, 54, 102801(2017).

[20] Loffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C], 448-456(2015).

[21] Uddin M S, Tahtali M, Lambert A J et al. Speckle-reduction algorithm for ultrasound images in complex wavelet domain using genetic algorithm-based mixture model[J]. Applied Optics, 55, 4024-4035(2016).

[22] Singh P, Mukundan R, de Ryke R. Synthetic models of ultrasound image formation for speckle noise simulation and analysis[C], 278-284(2017).

[23] Tsui P H, Wan Y L, Tai D I et al. Effects of estimators on ultrasound Nakagami imaging in visualizing the change in the backscattered statistics from a Rayleigh distribution to a pre-Rayleigh distribution[J]. Ultrasound in Medicine & Biology, 41, 2240-2251(2015).

[24] Loupas T. Digital image processing for noise reduction in medical ultrasonics[D](1988).

[25] Zhang C, Woodland P C. DNN speaker adaptation using parameterised sigmoid and ReLU hidden activation functions[C], 5300-5304(2016).

[26] Ito Y. Representation of functions by superpositions of a step or sigmoid function and their applications to neural network theory[J]. Neural Networks, 4, 385-394(1991).

[27] Schmidt U, Roth S. Shrinkage fields for effective image restoration[C], 2774-2781(2014).

[28] Qu H C, Tang B W, Yuan G S. Improved super-resolution image reconstruction algorithm[J]. Laser & Optoelectronics Progress, 58, 0210018(2021).