[1] MAHLER S, ELIEZER Y, YLMAZ H, et al. Fast laser speckle suppression with an intracavity diffuser[J]. Nanophotonics, 2020, 10(1): 129-136. doi: 10.1515/nanoph-2020-0390
[2] GUO Y, DENG J, LI J, et al. Static laser speckle suppression using liquid light guides[J]. Optics Express, 2021, 29(9): 14135-14150. doi: 10.1364/OE.425587
[3] 黄艳, 陈怀熹. 激光投影显示中复合散斑抑制方法的研究[J]. 激光技术, 2024, 48(2): 274-280.HUANG Y, CHEN H X. Research on composite speckle suppression methods in laser projection display[J]. Laser Technology, 2024, 48(2): 274-280(in Chinese).
[4] 郜魏柯, 杜小平, 王阳, 等. 激光散斑目标探测技术综述[J]. 中国光学, 2020, 13(6): 1182-1193.GAO W K, DU X P, WANG Y, et al. Overview of laser speckle target detection technology[J]. China Optics, 2020, 13(6): 1182-1193(in Chinese).
[5] ROWLEY L J, THAI T Q, JARRETT S R, et al. Correcting for digital image correlation speckle inversion at high temperature using color cameras[J]. Applied Optics, 2022, 61(27): 7948-7957. doi: 10.1364/AO.463480
[6] 郜魏柯, 杜小平, 王阳, 等. 微粗糙表面参数对激光散斑场的影响规律分析[J]. Acta Optica Sinica, 2021, 41(11): 1103001.GAO W K, DU X P, WANG Y, et al. Analysis of the influence of micro rough surface parameters on laser speckle field[J]. Acta Optica Sinica, 2021, 41(11): 1103001(in Chinese).
[7] ADJABI I, OUAHABI A, BENZAOUI A, et al. Past, present, and future of face recognition: A review[J]. Electronics, 2020, 9(8): 1188-1239. doi: 10.3390/electronics9081188
[8] KORTLI Y, JRIDI M, AL FALOU A, et al. Face recognition systems: A survey[J]. Sensors, 2020, 20(2): 342-377. doi: 10.3390/s20020342
[9] GAO Q, TONG Z, MA Y, et al. Flexible and lightweight speckle noise suppression module based on generation of dynamic speckles with multimode fiber and macro fiber composite[J]. Optics and Laser Technology, 2020, 123: 105941. doi: 10.1016/j.optlastec.2019.105941
[10] 贺锋涛, 曹金凤, 王晓琳, 等. 基于激光散斑的应力传感系统[J]. 红外与激光工程, 2015, 44(12): 3729-3733. doi: 10.3969/j.issn.1007-2276.2015.12.039HE F T, CAO J F, WANG X L, et al. Stress sensing system based on laser speckle[J]. Infrared and Laser Engineering, 2015, 44(12): 3729-3733(in Chinese). doi: 10.3969/j.issn.1007-2276.2015.12.039
[11] VALENT E, SILBERBERG Y. Scatterer recognition via analysis of speckle patterns[J]. Optica, 2018, 5(2): 204-207. doi: 10.1364/OPTICA.5.000204
[12] 苗希彩. 蓝绿激光海洋湍流及其下行信道传输特性研究[D]. 西安: 西安电子科技大学, 2017: 1-106.MIAO X C. Research on the characteristics of blue green laser ocean turbulence and its downstream channel transmission[D]. Xi'an: Xi'an University of Electronic Science and Technology, 2017: 1-106(in Chinese).
[13] WU Y, ZHANG Y, ZHU Y. Average intensity and directionality of partially coherent model beams propagating in turbulent ocean[J]. Journal of the Optical Society of America, 2016, A33(8): 1451-1458.
[14] BAYKAL Y. Scintillation index in strong oceanic turbulence[J]. Optics Communications, 2016, 375: 15-18. doi: 10.1016/j.optcom.2016.05.002
[15] 贺锋涛, 李佳琪, 张建磊, 等. 海洋湍流下波长分集无线光通信系统性能分析[J]. 红外与激光工程, 2021, 50(12): 20210131.HE F T, LI J Q, ZHANG J L, et al. Performance analysis of wavelength diversity wireless optical communication systems under ocean turbulence[J]. Infrared and Laser Engineering, 2021, 50(12): 20210131 (in Chinese).
[16] YUAN Y, BI Y, SUN M Y, et al. Speckle evaluation in laser display: From speckle contrast to speckle influence degree[J]. Optics Communications, 2020, 454: 124405. doi: 10.1016/j.optcom.2019.124405
[17] YUAN Y, BI Y, SUN M Y, et al. Quantification of the effects of time-varying speckle patterns on speckle images using a modified speckle influence degree method[J]. Optics Communications, 2020, 463: 125368. doi: 10.1016/j.optcom.2020.125368
[18] LEE S G, SUNG Y, KIM Y G, et al. Variations of AlexNet and GoogLeNet to improve Korean character recognition performance[J]. Journal of Information Processing Systems, 2018, 14(1): 205-217.
[19] YANG N, ZHANG Z K, YANG J H, et al. A convolutional neural network of googlenet applied in mineral prospectivity prediction based on multi-source geoinformation[J]. Natural Resources Research, 2021, 30(6): 3905-3923. doi: 10.1007/s11053-021-09934-1
[20] ZHONG Y, HUANG B, TANG C. Classification of cassava leaf disease based on a non-balanced dataset using transformer-embedded ResNet[J]. Agriculture, 2022, 12(9): 1360-1377. doi: 10.3390/agriculture12091360
[21] LEE K S, JUNG S K, RYU J J, et al. Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs[J]. Journal of Clinical Medicine, 2020, 9(2): 392-404. doi: 10.3390/jcm9020392
[22] LI Z, LI F, ZHU L, et al. Vegetable recognition and classification based on improved VGG deep learning network model[J]. International Journal of Computational Intelligence Systems, 2020, 13(1): 559-564. doi: 10.2991/ijcis.d.200425.001
[23] LI B, HE Y. An improved ResNet based on the adjustable shortcut connections[J]. IEEE Access, 2018, 6(99): 18967-18974.