[1] ZHANG X, CHEN Zh K, GAO J, et al. A two-stage deep transfer learning model and its application for medical image processing in traditional medicine[J]. Knowledge-Based Systems, 2022, 239(5): 108060.
[2] HOLTKAMP A, ELHENNAWY K, ORO J, et al. Clinical medicine generalizability of deep learning models for caries detection in near-infrared light transillumination images[J]. Journal of Clinical Medicine, 2021, 10(5): 961. doi: 10.3390/jcm10050961
[3] 张艳月, 张宝华, 赵云飞, 等. 基于双通道深度密集特征融合的遥感影像分类[J]. 激光技术, 2021, 45(1): 73-79.ZHANG Y Y, ZHANG B H, ZHAO Y F, et al. Remote sensing image classification based on dual-channel deep dense feature fusion[J]. Laser Technology, 2021, 45(1): 73-79(in Chinese).
[4] XUE H L, CHEN X, ZHANG R, et al. Deep learning-based maritime environment segmentation for unmanned surface vehicles using superpixel algorithms[J]. Journal of Marine Science and Engineering, 2021, 9(12): 1329. doi: 10.3390/jmse9121329
[5] MA G, LLANESl A, IMBERNON B, et al. Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning[J]. The Journal of Supercomputing, 2021, 77(9): 1-23.
[6] WU Y F, WU J C, HAO R, et al. Research progress of particle field digital holography based on deep learning[J]. Journal of Applied Optics, 2020, 41(4): 662-674. doi: 10.5768/JAO202041.0409003
[7] 向东, 桂进斌, 刘超, 等. 数字全息波前准确重建的实验研究[J]. 激光技术, 2017, 41(3): 406-410.XIANG D, GUI J B, LIU Ch, et al. Experiment research of accurate wavefront reconstruction of digital holography[J]. Laser Technology, 2017, 41(3): 406-410(in Chinese).
[8] LIU Y P, ZHANG T, SU J, et al. Reconstruction resolution enhancement of epism based holographic stereogram with hogel spatial multiplexing[J]. Chinese Physics, 2022, 31(4): 310-319.
[9] LI F Q, ZHAO M, TIAN Z M, et al. Compressive ghost imaging through scattering media with deep learning[J]. Optics Express, 2020, 28(12): 17395-17408. doi: 10.1364/OE.394639
[10] YIN D, GU Z Z, ZHANG Y R. Digital holographic reconstruction based on deep learning framework with unpaired data[J]. IEEE Photonics Journal, 2020, 12(2): 1-12.
[11] WU Y C, RIVENSON Y, ZHANG Y B, et al. Extended depth-of-field in holographic imaging using deeplearning-based autofocusing and phase recovery[J]. Optica, 2018, 5(6): 704-710. doi: 10.1364/OPTICA.5.000704
[12] REN Zh B, XU Zh M, LAM E Y M. End-to-end deep learning framework for digital holographic reconstruction[J]. Advanced Photonics, 2019, 1(1): 016004.
[13] WANG H, LYU M, SITU G. eHoloNet: A learning-based end-to-end approach for in-line digital holographic reconstruction[J]. Optics Express, 2018, 26(18): 22603-22614. doi: 10.1364/OE.26.022603
[14] WANG K Q, DOU J Z, QIAN K M, et al. Y-Net: A one-to-two deep learning framework for digital holographic reconstruction[J]. Optics Letters, 2019, 44(19): 4765-4768. doi: 10.1364/OL.44.004765
[15] 李菊, 李军. 基于U-Net网络的单幅全息图重建方法研究[J]. 激光杂志, 2020, 41(1): 96-99.LI J, LI J. Research on single hologram reconstruction method based on U-Net network[J]. Laser Journal, 2020, 41(1): 96-99(in Chin-ese).
[16] 肖文, 李解, 潘锋, 等. 基于USENet实现数字全息细胞再现相位像超分辨重构[J]. 光子学报, 2020, 49(6): 173-184.XIAO W, LI J, PAN F, et al. Super-resolution in digital holographic phase cell image based on usenet[J]. Acta Photonica Sinica, 2020, 49(6): 173-184(in Chinese).
[17] PIRONE D, SIRICO D, MICCIO L, et al. Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning[J]. Lab on a Chip, 2022, 22(4): 793-804. doi: 10.1039/D1LC01087E
[18] NAYFE B H, ABDULLAH S N H S, SULAIMAN R, et al. Optimized leakyrelu for handwritten arabic character recognition using convolution neural networks[J]. Multimedia Tools and Applications, 2022, 81(2): 2065-2094. doi: 10.1007/s11042-021-11593-6
[19] 刘飞飞. 数字全息显微中的高精度位相重建算法及实验研究[D]. 邯郸: 河北工程大学, 2014: 42-124.LUI F F. Investigation of exact phase reconstruction algorithm and expeiments in digital holographic microscopy[D]. Handan: Hebei University of Engineering, 2014: 42-124(in Chinese).
[20] 宋修法, 于梦杰, 王华英, 等. 物光与参考光强度比对数字全息再现像质的影响[J]. 激光技术, 2014, 38(6): 859-862.SONG X F, YU M J, WANG H Y, et al. Effect of reference intensity ratio to object on reconstructed image quality in digital holography[J]. Laser Technology, 2014, 38(6): 859-862(in Chinese).
[21] HAN K, WANG Y, XU C, et al. Ghostnets on heterogeneous devices via cheap operations[J]. International Journal of Computer Vision, 2022, 130(4): 1050-1069. doi: 10.1007/s11263-022-01575-y
[22] LIU H, LI D, JIANG B, et al. Mgbn-yolo: A faster light-weight object detection model for robotic grasping ofbolster spring based on image-based visual servoing[J]. Journal of Intelligent & Robotic Systems, 2022, 104(4): 77.
[23] WEI B, SHEN X, YUAN Y. Remote sensing scene classification based on improved ghostnet[J]. Journal of Physics Conference Series, 2020, 1621(1): 012091. doi: 10.1088/1742-6596/1621/1/012091
[24] WEI Y, YUAN Q, SHEN H, et al. Boosting the accuracy of multispectral image pansharpening by learning a deep residual network[J]. Geoscience and Remote Sensing Letters, 2017, 14(10): 1795-1799.
[25] JIANG Y, CHEN L, ZHANG H, et al. Breast cancer histopathological image classification using convolutional neural networks with small se-resnet module[J]. Plos One, 2019, 14(3): 10214587.
[26] 毛志荣, 都云程, 肖诗斌, 等. 基于ECA-Net与多尺度结合的细粒度图像分类方法[J]. 计算机应用研究, 2021, 38(11): 3484-3488.MAO Zh R, DU Y Ch, XIAO Sh B, et al. Fine-grained image classification method based on ECA-net and multi-scale[J]. Application Research of Computers, 2021, 38(11): 3484-3488(in Chinese).
[27] XUE H, SUN M H, LIANG Y H. ECANet: Explicit cyclic attention-based network for video saliency prediction[J]. Neurocomputing, 2022, 468(C): 233-244.