Page 152 - 《软件学报》2024年第4期

1730 软件学报 2024 年第 35 卷第 4 期

[9] Howard AG, Zhu M, Chen B, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications.
arXiv:1704.04861, 2017.
[10] Zhang X, Zhou X, Lin M, et al. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In: Proc. of
the IEEE/CVF Conf. on Computer Vision and Pattern Recognition. 2018. 6848−6856.
[11] Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations. In: Proc. of the IEEE/CVF Conf. on Computer
Vision and Pattern Recognition. 2020. 1577−1586.
[12] Mehta S, Mohammad R. MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer. arXiv:2110.02178,
2021.
[13] Lee JY, Park RH. Complex-valued disparity: Unified depth model of depth from stereo, depth from focus, and depth from defocus
based on the light field gradient. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2021, 43(3): 830−841.
[14] Muhammad M, Choi TS. Sampling for shape from focus in optical microscopy. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 2012, 34(3): 564−573.
[15] Jeon HG, Surh J, Im S, et al. Ring difference filter for fast and noise robust depth from focus. IEEE Trans. on Image Processing,
2020, 29: 1045−1060.
[16] Yan T, Hu Z, Qian YH, et al. 3D shape reconstruction from multifocus image fusion using a multidirectional modified laplacian
operator. Pattern Recognition, 2020, 98: 107065.
[17] Yan T, Wu P, Qian YH, et al. Multiscale fusion and aggregation pcnn for 3D shape recovery. Information Sciences, 2020, 536:
277−297.
[18] Minhas R, Mohammed AA, Wu QM. Shape from focus using fast discrete curvelet transform. Pattern Recognition, 2011, 44(4):
839−853.
[19] Ali U, Muhammad TM. Robust focus volume regularization in shape from focus. IEEE Trans. on Image Processing, 2021, 30:
7215−7227.
[20] Moeller M, Benning M, Schnlieb C, et al. Variational depth from focus reconstruction. IEEE Trans. on Image Processing, 2015,
24(12): 5369−5378.
[21] Hu, J, Li S, Gang S. Squeeze-and-excitation networks. In: Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern
Recognition. 2018. 7132−7141.
[22] Daquan Z, Hou Q, Chen Y, et al. Rethinking bottleneck structure for efficient mobile network design. arXiv:2007.02269, 2020.
[23] Chollet F. Xception: Deep learning with depthwise separable convolutions. In: Proc. of the IEEE Conf. on Computer Vision and
Pattern Recognition. 2017. 1800−1807.
[24] Zhang T, Qi GJ, Xiao B, et al. Interleaved group convolutions for deep neural networks. arXiv:1707.02725, 2017.
[25] Tan M, Le QV. EfficientNet: Rethinking model scaling for convolutional neural networks. arXiv:1905.11946, 2019.
[26] Han K, Wang Y, Zhang Q, et al. Model rubik’s cube: Twisting resolution, depth and width for TinyNets. arXiv:2010.14819, 2020.
[27] Ma N, Zhang X, Zheng H, et al. ShuffleNet V2: Practical guidelines for efficient CNN architecture design. In: Proc. of the
European Conf. on Computer Vision. 2018. 122−138.
[28] Andrew H, Mark S, Grace C, et al. Searching for MobileNetV3. In: Proc. of the IEEE/CVF Int’l Conf. on Computer Vision. 2019.
1314−1324.
[29] Chen J, Kao S, He H, et al. Run, don’t walk: Chasing higher FLOPS for faster neural networks. In: Proc. of the IEEE/CVF Conf. on
Computer Vision and Pattern Recognition. 2023.
[30] Pavan K, Vasu A, Gabriel J, et al. MobileOne: An improved one millisecond mobile backbone. In: Proc. of the IEEE/CVF Conf. on
Computer Vision and Pattern Recognition. 2023.
[31] Chen Y, Dai X, Chen D, et al. Mobile-former: Bridging MobileNet and transformer. In: Proc. of the IEEE/CVF Conf. on Computer
Vision and Pattern Recognition. 2022. 5260−5269.
[32] Pentland AP. A new sense for depth of field. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1987, 4: 523−531.
[33] Won C, Jeon H. Learning depth from focus in the wild. In: Proc. of the European Conf. on Computer Vision. 2022. 1−18.
[34] Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Communications of the
ACM, 2017, 60(624): 84−90.

附中文参考文献:
[3] 闫涛, 钱宇华, 李飞江, 等. 三维时频变换视角的智能微观三维形貌重建方法. 中国科学: 信息科学, 2023, 53: 282−308. [doi:
10.1360/SSI-2021-0386]