• Optics and Precision Engineering
  • Vol. 31, Issue 6, 962 (2023)
Yangwei FU1,2, Jin ZHANG1,2,3,*, Zhenxi SUN1,2, Rui ZHANG1,2..., Weishi LI1,2,3 and Haojie XIA1,2,3|Show fewer author(s)
Author Affiliations
  • 1School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology, Hefei230009, China
  • 2Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, Hefei30009, China
  • 3Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei20009, China
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    DOI: 10.37188/OPE.20233106.0962 Cite this Article
    Yangwei FU, Jin ZHANG, Zhenxi SUN, Rui ZHANG, Weishi LI, Haojie XIA. Design of channel attention network and system for micro target measurement[J]. Optics and Precision Engineering, 2023, 31(6): 962 Copy Citation Text show less
    References

    [1] H YU. Image-type displacement measurement resolution improvement without magnification imaging. Measurement Science and Technology, 33(2022).

    [2] 2王冬云, 唐楚, 鄂世举, 等. 基于导向滤波Retinex和自适应Canny的图像边缘检测[J]. 光学 精密工程, 2021, 29(2): 443-451. doi: 10.37188/OPE.20212902.0443WANGD Y, TANGCH, E SH J, et al. Image edge detection based on guided filter Retinex and adaptive Canny[J]. Opt. Precision Eng., 2021, 29(2): 443-451.(in Chinese). doi: 10.37188/OPE.20212902.0443

    [3] 3刘宇涵, 闫河, 陈早早, 等. 强噪声下自适应Canny算子边缘检测[J]. 光学 精密工程, 2022, 30(3): 350-362. doi: 10.37188/OPE.20223003.0350LIUY H, YANH, CHENZ Z, et al. Adaptive Canny operator edge detection under strong noise[J]. Opt. Precision Eng., 2022, 30(3): 350-362.(in Chinese). doi: 10.37188/OPE.20223003.0350

    [4] S SHARMA, J VENTURA, S D’AMICO. Robust model-based monocular pose initialization for noncooperative spacecraft rendezvous. Journal of Spacecraft and Rockets, 55, 1414-1429(2018).

    [5] W WANG, X N LU, Z Z HE et al. Using convolutional neural network for intelligent SAM inspection of flip chips. Measurement Science and Technology, 32, 115022(2021).

    [6] 6朱福珍, 刘越, 黄鑫, 等. 改进的稀疏表示遥感图像超分辨重建[J]. 光学 精密工程, 2019, 27(3): 718-725. doi: 10.13482/j.issn1001-7011.2019.05.004ZHUF ZH, LIUY, HUANGX, et al. Remote sensing image super-resolution based on improved sparse representation[J]. Opt. Precision Eng., 2019, 27(3): 718-725.(in Chinese). doi: 10.13482/j.issn1001-7011.2019.05.004

    [7] 7周涛, 霍兵强, 陆惠玲, 等. 融合多尺度图像的密集神经网络肺部肿瘤识别算法[J]. 光学 精密工程, 2021, 29(7): 1695-1708. doi: 10.37188/OPE.20212907.1695ZHOUT, HUOB Q, LUH L, et al. Lung tumor image recognition algorithm with densenet fusion multi-scale images[J]. Opt. Precision Eng., 2021, 29(7): 1695-1708.(in Chinese). doi: 10.37188/OPE.20212907.1695

    [8] P THÉVENAZ, M UNSER. Linear interpolation revitalized. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 13, 710-719(2004).

    [9] R R SCHULTZ, R L STEVENSON. Extraction of high-resolution frames from video sequences. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 5, 996-1011(1996).

    [10] 10彭真明, 景亮, 何艳敏, 等. 基于多尺度稀疏字典的多聚焦图像超分辨融合[J]. 光学 精密工程, 2014, 22(1): 169-176. doi: 10.3788/ope.20142201.0169PENGZ M, JINGL, HEY M, et al. Superresolution fusion of multi-focus image based on multiscale sparse dictionary[J]. Opt. Precision Eng., 2014, 22(1): 169-176.(in Chinese). doi: 10.3788/ope.20142201.0169

    [11] Y TANG, Y YUAN. Image pair analysis with matrix-value operator. IEEE Transactions on Cybernetics, 45, 2042-2050(2015).

    [12] C DONG, C C LOY, K M HE et al. Learning a deep convolutional network for image super-resolution. Computer Vision, 184-199(2014).

    [13] C DONG, C C LOY, X O TANG. Accelerating the super-resolution convolutional neural network. Computer Vision, 391-407(2016).

    [14] J KIM, J K LEE, K M LEE. Accurate Image Super-Resolution Using Very Deep Convolutional Networks, 1646-1654(2016).

    [15] K M HE, X Y ZHANG, S Q REN et al. Deep Residual Learning for Image Recognition, 770-778(2016).

    [16] 16谷雨, 刘俊, 沈宏海, 等. 基于改进多尺度分形特征的红外图像弱小目标检测[J]. 光学 精密工程, 2020, 28(6): 1375-1386. doi: 10.3788/ope.20202806.1375GUY, LIUJ, SHENH H, et al. Infrared dim-small target detection based on an improved multiscale fractal feature[J]. Opt. Precision Eng., 2020, 28(6): 1375-1386.(in Chinese). doi: 10.3788/ope.20202806.1375

    [17] C SZEGEDY, W LIU, Y Q JIA et al. Going Deeper with Convolutions, 1-9(2015).

    [18] K SIMONYAN, A ZISSERMAN. Very deep convolutional networks for large-scale image recognition. arXiv, 1409-1556(2014). https://arxiv.org/abs/1409.1556

    [19] G HUANG, Z LIU, L VAN DER MAATEN et al. Densely Connected Convolutional Networks, 2261-2269(2017).

    [20] J S CHOI, M KIM. A Deep Convolutional Neural Network with Selection Units for Super-Resolution, 1150-1156(2017).

    [21] H KIM et al. Enhanced Deep Residual Networks for Single Image Super-Resolution, 1132-1140(2017).

    [22] 22朱威, 王立凯, 靳作宝, 等. 引入注意力机制的轻量级小目标检测网络[J]. 光学 精密工程, 2022, 30(8): 998-1010. doi: 10.37188/OPE.20223008.0998ZHUW, WANGL K, JINZ B, et al. Lightweight small object detection network with attention mechanism[J]. Opt. Precision Eng., 2022, 30(8): 998-1010.(in Chinese). doi: 10.37188/OPE.20223008.0998

    [23] 23鞠默然, 罗海波, 刘广琦, 等. 采用空间注意力机制的红外弱小目标检测网络[J]. 光学 精密工程, 2021, 29(4): 843-853. doi: 10.37188/OPE.20212904.0843JUM R, LUOH B, LIUG Q, et al. Infrared dim and small target detection network based on spatial attention mechanism[J]. Opt. Precision Eng., 2021, 29(4): 843-853.(in Chinese). doi: 10.37188/OPE.20212904.0843

    [24] W Z SHI, J CABALLERO, F HUSZÁR et al. Real-time Single Image and Video Super-Resolution Using An Efficient Sub-Pixel Convolutional Neural Network, 1874-1883(2016).

    [25] X T LUO, Y XIE, Y L ZHANG et al. LatticeNet: towards lightweight image super-resolution with lattice block. Computer Vision, 272-289(2020).

    Yangwei FU, Jin ZHANG, Zhenxi SUN, Rui ZHANG, Weishi LI, Haojie XIA. Design of channel attention network and system for micro target measurement[J]. Optics and Precision Engineering, 2023, 31(6): 962
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