Page 492 - 《软件学报》2024年第6期

3068 软件学报 2024 年第 35 卷第 6 期

Graphics, 2021, 40(6): 239. [doi: 10.1145/3478513.3480553]
[33] Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proc. of the 2018 IEEE/CVF Conf. on Computer Vision and Pattern
Recognition. Salt Lake City: IEEE, 2018. 7132–7141. [doi: 10.1109/CVPR.2018.00745]
[34] He KM, Zhang XY, Ren SQ, Sun, J. Deep residual learning for image recognition. In: Proc. of the 2016 IEEE Conf. on Computer Vision
and Pattern Recognition. Las Vegas: IEEE, 2016. 770–778. [doi: 10.1109/CVPR.2016.90]
[35] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: From error visibility to structural similarity. IEEE Trans. on
Image Processing, 2004, 13(4): 600–612. [doi: 10.1109/TIP.2003.819861]
[36] Zhang R, Isola P, Efros AA, Shechtman E, Wang O. The unreasonable effectiveness of deep features as a perceptual metric. In: Proc. of
the 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018. 586–595. [doi: 10.1109/CVPR.
2018.00068]
[37] Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen J, Lin ZM, Gimelshein N, Antiga L, Desmaison A, Köpf A, Yang
E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai JJ, Chintala, S. PyTorch: An imperative style, high-
performance deep learning library. In: Proc. of the 33rd Int ’l Conf. on Neural Information Processing Systems. Vancouver: Curran
Associates Inc., 2019. 721.
[38] Kingma DP, Ba J. Adam: A method for stochastic optimization. In: Proc. of the 3rd Int’l Conf. on Learning Representations. San Diego:
覃浩宇(1998－), 男, 硕士生, 主要研究领域为计
ICLR, 2015.
[39] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proc. of the 13th Int’l Conf. on
Artificial Intelligence and Statistics. Sardinia: JMLR.org, 2010. 249–256.
[40] Open Neural Network Exchange. 2022. https://onnx.ai/.ONNX
[41] NVIDIA TensorRT. 2022. https://github.com/NVIDIA/TensorRT.NVIDIA
[42] NVIDIA. 2022. https://developer.nvidia.com/cudnn.NVIDIAcuDNN

附中文参考文献:
[22] 丁玲, 丁世飞, 张健, 张子晨. 使用VGG能量损失的单图像超分辨率重建. 软件学报, 2021, 32(11): 3659–3668. http://www.jos.org.cn/
1000-9825/6053.htm [doi: 10.13328/j.cnki.jos.006053]
[23] 潘宗序, 禹晶, 肖创柏, 孙卫东. 基于自适应多字典学习的单幅图像超分辨率算法. 电子学报, 2015, 43(2): 209–216. [doi: 10.3969/j.
issn.0372-2112.2015.02.001]
[30] 杜文俊, 冯结青. 面向延迟着色的统一反走样算法. 计算机辅助设计与图形学学报, 2016, 28(1): 58–67. [doi: 10.3969/j.issn.1003-
9775.2016.01.008]
[31] 邵鹏, 周伟, 李光泉, 吴志健. 一种后处理式的改进抗锯齿算法. 计算机科学, 2018, 45(11A): 218–221, 225.

张浩南(2000－), 男, 硕士生, 主要研究领域为计 傅锡豪(1997－), 男, 硕士生, 主要研究领域为实
算机图形学. 时渲染.

过洁(1986－), 男, 博士, 副研究员, CCF 高级会 郭延文(1980－), 男, 博士, 教授, 博士生导师,
员, 主要研究领域为计算机图形学, 虚拟现实. CCF 专业会员, 主要研究领域为计算机图形学,
三维视觉.

算机图形学.