Review of New Face Occlusion Inpainting Technology Research

doi:10.3778/j.issn.1673-9418.2103092

Abstract

Abstract:

In criminal investigation, if the face image of a suspect is occluded and the face feature points are corrupted, the precise removal of the occluded area becomes an important step in improving face recognition technology. Therefore, face occlusion inpainting has important research significance. The recent progress of face occlusion inpainting technology is described, and based on the deep learning-based image restoration algorithm first proposed in 2016, various face occlusion inpainting fusion algorithms proposed by scholars from 2017 to the present are introduced. Firstly, the existing algorithms are classified into random occlusion and regular occlusion face restoration according to the different ways of occlusion, then further classified into based on convolutional neural network (CNN) and based on generative adversarial network (GAN) according to the different predictive generation networks in the algorithms. This paper analyzes various types of fusion algorithms in terms of model network characteristics, advantages and disadvantages as well as applicable scenarios, and gives some suggestions for the selection of fusion algorithms. The regular occlusion algorithms and random occlusion algorithms are compared and summarized in terms of network structure and applicability range. Then it introduces and summarizes the commonly used image restoration effect evaluation indices and datasets, and by listing the experimental results of various restoration algorithms, it refines and analyzes their quantitative indices and visual effects, and illustrates that the face occlusion inpainting technology has made great progress in recent years. Finally, the future development trend of face occlusion inpainting technology is pointed out from five aspects, such as dataset, algorithm and evaluation index, by combining the existing algorithms and practical requirements.

Key words: face occlusion inpainting, deep learning, convolutional neural network (CNN), generative adversarial network (GAN)

摘要：

刑侦工作中，若犯罪嫌疑人的人脸图像存在遮挡，人脸特征点遭到破坏，精确去除遮挡区域成为提高人脸识别技术的重要一步。因此，人脸去遮挡有着重要的研究意义。对人脸去遮挡技术最新进展进行阐述，并基于2016年首次提出的基于深度学习图像修复算法，介绍从2017年至今学者们提出的各类人脸去遮挡融合算法。首先根据遮挡方式的不同将现有算法分类为随机遮挡和规则遮挡的人脸修复，接着根据算法中预测生成网络的不同，进一步分为基于卷积神经网络（CNN）和基于生成式对抗网络（GAN），并对各类融合算法从模型网络特点、优缺点以及适用场景进行分析，给出一些融合算法的选择建议，从网络结构和适用范围方面对比总结规则遮挡算法和随机遮挡算法。然后介绍并汇总常用的图像修复效果评价指标和数据集，通过列举各类修复算法的实验结果，提炼并分析其定量指标和视觉效果，说明了近年来的人脸去遮挡技术取得了较大的进展。最后结合现有算法和实际需求，从数据集、算法、评价指标等五方面指出人脸去遮挡技术的未来发展趋势。

关键词: 人脸去遮挡, 深度学习；卷积神经网络（CNN）；生成式对抗网络（GAN）

LIU Ying, ZHANG Yixuan, SHE Jianchu, WANG Fuping, LIM Kengpang. Review of New Face Occlusion Inpainting Technology Research[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(10): 1773-1794.

刘颖, 张艺轩, 佘建初, 王富平, 林庆帆. 人脸去遮挡新技术研究综述[J]. 计算机科学与探索, 2021, 15(10): 1773-1794.

References

[1] ZHANG C P, SU G D. Human face recognition: a survey[J]. Journal of Image and Graphics, 2000, 5(11): 885-894.
张翠平, 苏光大. 人脸识别技术综述[J]. 中国图象图形学报, 2000, 5(11): 885-894.
[2] PENG X J, QIAO Y. Advances and challenges in facial expression analysis[J]. Journal of Image and Graphics, 2020, 25(11): 2337-2348.
彭小江, 乔宇. 面部表情分析进展和挑战[J]. 中国图象图形学报, 2020, 25(11): 2337-2348.
[3] SHU Y, MAO L B, CHEN S, et al. Self-supervised learning and generative adversarial network-based facial attribute recognition with small sample size training[J]. Journal of Image and Graphics, 2020, 25(11): 2391-2403.
疏颖, 毛龙彪, 陈思, 等. 结合自监督学习和生成对抗网络的小样本人脸属性识别[J]. 中国图象图形学报, 2020, 25(11): 2391-2403.
[4] DUAN Y K. Deep learning based face recognition technology in security field[J]. Security in China, 2017(11): 72-74.
段应奎. 基于深度学习的人脸识别技术在安防领域的应用[J]. 中国安防, 2017(11): 72-74.
[5] DONG Y H, ZHANG S M, ZHAO J L. Review of occlusion face recognition method[J]. Computer Engineering and Applications, 2020, 56(9): 1-12.
董艳花, 张树美, 赵俊莉. 有遮挡人脸识别方法综述[J]. 计算机工程与应用, 2020, 56(9): 1-12.
[6] LIU L. Face image restoration research based on context encoders and CGAN joint optimization[D]. Xiangtan: Xiangtan University, 2018.
刘恋. 基于语义编码器和CGAN联合优化的人脸缺损图像修复研究[D]. 湘潭: 湘潭大学, 2018.
[7] EIHARROUSS O, AIMAADEED N, AL-MáADEED S, et al. Image inpainting: a review[J]. Neural Processing Letters, 2020, 51(2): 2007-2028.
[8] BARNES C, SHECHTMAN E, FINKELSTEIN A, et al. PatchMatch: a randomized correspondence algorithm for structural image editing[J]. ACM Transactions on Graphics, 2009, 28(3): 24.
[9] BALLESTER C, BERTALMíO M, CASELLES V, et al. Filling-in by joint interpolation of vector fields and gray levels[J]. IEEE Transactions on Image Processing, 2001, 10(8): 1200-1211.
[10] BERTALMíO M, SAPIRO G, CASELLES V, et al. Image inpainting[C]//Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, New Orleans, Jul 23-28, 2000. New York: ACM, 2000: 417-424.
[11] PATHAK D, KR?HENBüHL P, DONAHUE J, et al. Context encoders: feature learning by inpainting[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Jun 27-30, 2016. Washington: IEEE Computer Society, 2016: 2536-2544.
[12] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the Annual Conference on Neural Information Processing Systems 2014, Montreal, Dec 8-13, 2014. Red Hook: Curran Associates, 2014: 2672-2680.
[13] IIZUKA S, SIMO-SERRA E, ISHIKAWA H. Globally and locally consistent image completion[J]. ACM Transactions on Graphics, 2017, 36(4): 1-14.
[14] YU J H, LIN Z, YANG J M, et al. Generative image inpainting with contextual attention[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-22, 2018. Washington: IEEE Computer Society, 2018: 5505-5514.
[15] WANG P, YE X Y, WANG T, et al. Face recognition based on double variation and double space local directional pattern[J]. Computer Engineering and Applications, 2021, 57(4): 91-99.
王鹏, 叶学义, 王涛, 等. 双偏差双空间局部方向模式的人脸识别[J]. 计算机工程与应用, 2021, 57(4): 91-99.
[16] LIU Y, SHE J C, GONG Y C, et al. Survey of facial completion techniques based on deep learning[J]. Application Research of Computers, 2021, 38(1): 9-14.
刘颖, 佘建初, 公衍超, 等. 基于深度学习的面部修复技术综述[J]. 计算机应用研究, 2021, 38(1): 9-14.
[17] WANG F P, LI W L, LIU Y, et al. Face inpainting algorithm combining edge information with gated convolution[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(1): 150-162.
王富平, 李文楼, 刘颖, 等. 结合边缘信息和门卷积的人脸修复算法[J]. 计算机科学与探索, 2021, 15(1): 150-162.
[18] CHEN J Z, WANG J, GONG X. Face image inpainting using cascaded generative adversarial networks[J]. Journal of University of Electronic Science and Technology of China, 2019, 48(6): 910-917.
陈俊周, 王娟, 龚勋. 基于级联生成对抗网络的人脸图像修复[J]. 电子科技大学学报, 2019, 48(6): 910-917.
[19] YANG W X, WANG M, ZHANG L. Semantic face image inpainting based on U-Net with dense blocks[J]. Journal of Computer Applications, 2020, 40(12): 3651-3657.
杨文霞, 王萌, 张亮. 基于密集连接块U-Net的语义人脸图像修复[J]. 计算机应用, 2020, 40(12): 3651-3657.
[20] XIE Z R, CUN Y P, JIANG D H, et al. Research on face image restoration based on least squares generative adversarial network[J]. Science & Technology Vision, 2020(22): 1-6.
谢卓然, 寸怡鹏, 姜德航, 等. 基于最小二乘生成对抗网络的人脸图像修复研究[J]. 科技视界, 2020(22): 1-6.
[21] ZHENG C X, CHAM T J, CAI J F. Pluralistic image completion[C]//Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, Jun 16-20, 2019. Piscataway: IEEE, 2019: 1438-1447.
[22] WANG Y, TAO X, QI X J, et al. Image inpainting via generative multi-column convolutional neural networks[C]//Proceedings of the Annual Conference on Neural Information Processing Systems 2018, Montreal, Dec 3-8, 2018. Red Hook: Curran Associates, 2018: 329-338.
[23] SONG Y H, YANG C, SHEN Y J, et al. SPG-Net: segmentation prediction and guidance network for image inpainting[C]//Proceedings of the British Machine Vision Conference 2018, Newcastle, Sep 3-6, 2018. Durham: BMVA Press, 2018: 97.
[24] CHEN Z Y, NIE S L, WU T F, et al. High resolution face completion with multiple controllable attributes via fully end-to-end progressive generative adversarial networks[J]. arXiv:1801.07632, 2018.
[25] ZENG Y H, FU J L, CHAO H Y, et al. Learning pyramid-context encoder network for high-quality image inpainting[C]//Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, Jun 16-20, 2019. Piscataway: IEEE, 2019: 1486-1494.
[26] LI Y J, LIU S F, YANG J M, et al. Generative face completion[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Washington: IEEE Computer Society, 2017: 5892- 5900.
[27] SONG L S, CAO J, SONG L X, et al. Geometry-aware face completion and editing[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Conference, the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park:AAAI, 2019: 2506-2513.
[28] ZHAO L, MO Q S, LIN S H, et al. UCTGAN: diverse image inpainting based on unsupervised cross-space translation[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, Jun 13-19, 2020. Piscataway: IEEE, 2020: 5740-5749.
[29] LAHIRI A, JAIN A K, AGRAWAL S, et al. Prior guided GAN based semantic inpainting[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, Jun 13-19, 2020. Piscataway: IEEE, 2020: 13693-13702.
[30] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//Proceedings of the 3rd International Conference on Learning Representations, San Diego, May 7-9, 2015: 1-14.
[31] KINGMA D P, WELLING M. Auto-encoding variational Bayes[C]//Proceedings of the 2nd International Conference on Learning Representations, Banff, Apr 14-16, 2014: 1-14.
[32] YEH R A, CHEN C, LIM T Y, et al. Semantic image inpainting with deep generative models[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Washington: IEEE Computer Society, 2017: 6882-6890.
[33] LIU Z W, LUO P, WANG X G, et al. Deep learning face attributes in the wild[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Dec 11-18, 2015. Washington: IEEE Computer Society, 2015: 3730-3738.
[34] GROSS R, MATTHEWS I, COHN J, et al. Multi-PIE[J]. Image and Vision Computing, 2010, 28(5): 807-813.
[35] RONNEBERGER O, FISCHER P, BROX T. U-NET: convolutional networks for biomedical image segmentation[C]//LNCS 9351: Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Oct 5-9, 2015. Berlin,Heidelberg: Springer, 2015: 234-241.
[36] YANG C, LU X, LIN Z, et al. High-resolution image inpainting using multi-scale neural patch synthesis[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Washington: IEEE Computer Society, 2017: 4076-4084.
[37] YAN Z Y, LI X M, LI M, et al. Shift-Net: image inpainting via deep feature rearrangement[C]//LNCS 11218: Proceedings of the 15th European Conference on Computer Vision, Munich, Sep 8-14, 2018. Cham: Springer, 2018: 3-19.
[38] SONG Y H, YANG C, LIN Z L, et al. Contextual-based image inpainting: infer, match, and translate[C]//LNCS 11206: Proceedings of the 15th European Conference on Computer Vision, Munich, Sep 8-14, 2018. Cham: Springer, 2018: 3-18.
[39] LIU G L, REDA F A, SHIH K J, et al. Image inpainting for irregular holes using partial convolutions[C]//LNCS 11215: Proceedings of the 15th European Conference on Computer Vision, Munich, Sep 8-14, 2018. Cham: Springer, 2018: 89-105.
[40] YANG Y, GUO X J, MA J Y, et al. LaFIn: generative landmark guided face inpainting[J]. arXiv:1911.11394, 2019.
[41] YU J H, LIN Z, YANG J M, et al. Free-form image inpainting with gated convolution[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Oct 27-Nov 3, 2019. Piscataway: IEEE, 2019: 4471-4480.
[42] LI J Y, WANG N, ZHANG L F, et al. Recurrent feature reasoning for image inpainting[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, Jun 16-18, 2020. Piscataway: IEEE, 2020: 7757-7765.
[43] HUI Z, LI J, WANG X M, et al. Image fine-grained inpainting[J]. arXiv:2002.02609, 2020.
[44] YUAN X W, PARK I K. Face de-occlusion using 3D morphable model and generative adversarial network[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Oct 27-Nov 2, 2019. Piscataway: IEEE, 2019: 10061-10070.
[45] NAZERI K, NG E, JOSEPH T, et al. Edgeonnect: generative image inpainting with adversarial edge learning[J]. arXiv: 1901.00212, 2019.
[46] GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved training of Wasserstein GANs[C]//Proceedings of the 2017 International Conference on Neural Information Processing Systems, Long Beach, Dec 4-9, 2017. Red Hook: Curran Associates, 2017: 5767-5777.
[47] JOLICOEUR-MARTINEAU A. The relativistic discriminator: a key element missing from standard GAN[J]. arXiv:1807. 00734, 2018.
[48] BLANZ V, VETTER T. A morphable model for the synthesis of 3D faces[C]//Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, Los Angeles, Aug 8-13, 1999. New York: ACM, 1999: 187-194.
[49] ZHU X Y, LEI Z, LIU X M, et al. Face alignment across large poses: a 3D solution[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Jun 27-30, 2016. Washington: IEEE Computer Society, 2016: 146-155.
[50] SAGONG M C, SHIN Y G, KIM S W, et al. PEPSI: fast image inpainting with parallel decoding network[C]//Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, Jun 16-20, 2019. Piscataway: IEEE, 2019: 11360-11368.
[51] HUO X Z, ZHANG W D, TIAN S W, et al. Parallel decoder image inpainting method for edge-terminal collaboration[J/OL]. Computer Engineering and Applications (2021-04-19) [2021-05-19]. http://kns.cnki.net/kcms/detail/11.2127.TP. 20210419.1320.009.html.
霍相佐, 张文东, 田生伟, 等. 面向边-端协同的并行解码器图像修复方法[J/OL]. 计算机工程与应用 (2021-04-19) [2021-05-19]. http://kns.cnki.net/kcms/detail/11.2127.TP. 20210419.1320.009.html.
[52] LUO H L, AO Y, YUAN P. Image inpainting using generative adversarial networks[J]. Acta Electronica Sinica, 2020, 48(10): 1891-1898.
罗会兰, 敖阳, 袁璞. 一种生成对抗网络用于图像修复的方法[J]. 电子学报, 2020, 48(10): 1891-1898.
[53] VORONIN V V, SIZYAKIN R A, MARCHUK V I, et al. Video inpainting of complex scenes based on local statistical model[C]//Proceedings of the Image Processing: Algorithms and Systems XIV, San Francisco, Feb 14-18, 2016: 1-6.
[54] HE K, NIU J H, SHEN C N, et al. Image inpainting algorithm with adaptive patch using SSIM[J]. Journal of Tianjin University (Science and Technology), 2018, 51(7): 763-767.
何凯, 牛俊慧, 沈成南, 等. 基于SSIM 的自适应样本块图像修复算法[J]. 天津大学学报(自然科学与工程技术版), 2018, 51(7): 763-767.
[55] HORé A, ZIOU D. Image quality metrics: PSNR vs. SSIM[C]//Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Aug 23-26, 2010. Washington: IEEE Computer Society, 2010: 2366-2369.
[56] SALIMANS T, GOODFELLOW I J, ZAREMBA W, et al. Improved techniques for training GANs[J]. arXiv:1606. 03498, 2016.
[57] HEUSEL M, RAMSAUER H, UNTERTHINER T, et al. GANs trained by a two time-scale update rule converge to a local Nash equilibrium[C]//Proceedings of the 2017 International Conference on Neural Information Processing Systems, Long Beach, Dec 4-9, 2017. Red Hook: Curran Associates, 2017: 6626-6637.
[58] PARK E, YANG J M, YUMER E, et al. Transformation-grounded image generation network for novel 3D view synthesis[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Washington: IEEE Computer Society, 2017: 702-711.
[59] DOWSON D C, LANDAU B V. The Fréchet distance between multivariate normal distributions[J]. Journal of Multivariate Analysis, 1982, 12(3): 450-455.
[60] KARRAS T, AILA T, LAINE S, et al. Progressive growing of GANs for improved quality, stability, and variation[J]. arXiv:1710.10196, 2017.
[61] ZHOU B L, LAPEDRIZA à, KHOSLA A, et al. Places: a 10 million image database for scene recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(6): 1452-1464.
[62] RUSSAKOVSKY O, DENG J, SU H, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211-252.
[63] DOERSCH C, SINGH S, GUPTA A, et al. What makes Paris look like Paris?[J]. ACM Transactions on Graphics, 2012, 31(4): 101.
[64] WANG Z, SIMONCELLI E P, BOVIK A C. Multiscale structural similarity for image quality assessment[C]//Proceedings of the 37th Asilomar Conference on Signals, Systems & Computers, Pacific Grove, Nov 9-12, 2003. Piscataway: IEEE, 2003: 1398-1402.
[65] LIU H Y, JIANG B, XIAO Y, et al. Coherent semantic attention for image inpainting[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Oct 27-Nov 2, 2019. Piscataway: IEEE, 2019: 4169-4178.
[66] REN Y R, YU X M, ZHANG R N, et al. StructureFlow: image inpainting via structure-aware appearance flow[C]//Proceedings of the 2019 IEEE International Conference on Computer Vision, Seoul, Oct 27-Nov 2, 2019. Piscataway:IEEE, 2019: 181-190.
[67] LI J Y, HE F X, ZHANG L F, et al. Progressive reconstruction of visual structure for image inpainting[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Oct 27-Nov 2, 2019. Piscataway: IEEE, 2019: 5961-5970.
[68] LIU Q Y, LU S H, LAN L Q. Research progress on occluded face detection methods[J]. Computer Engineering and Applications, 2020, 56(13): 33-46.
刘淇缘, 卢树华, 兰凌强. 遮挡人脸检测方法研究进展[J]. 计算机工程与应用, 2020, 56(13): 33-46.
[69] WANG Z Y, WANG G C, HUANG B J, et al. Masked face recognition dataset and application[J]. arXiv:2003.09093, 2020.
[70] CAO B, SHAN S G, ZHANG X H, et al. Baseline evaluations on the CAS-PEAL-R1 face database[C]//LNCS 3338: Proceedings of the 5th Chinese Conference on Biometric Recognition Advances in Biometric Person Authentication, Guangzhou, Dec 13-14, 2004. Berlin, Heidelberg: Springer, 2004: 370-378.
[71] DU L L. Research on occluded face inpainting[D]. Xi’an: Xi’an University of Technology, 2020.
杜利利. 遮挡人脸的修复方法研究[D]. 西安: 西安理工大学, 2020.
[72] GUO K Y, MA L P, HU W. Facial feature point detection and facial orientation calculation based on DCNN[J]. Computer Engineering and Applications, 2020, 56(4): 202-208.
郭克友, 马丽萍, 胡巍. 基于DCNN的人脸特征点检测及面部朝向计算[J]. 计算机工程与应用, 2020, 56(4): 202-208.
[73] JI Z, WANG H R, YU Y L, et al. A decadal survey of zero-shot image classification[J]. Scientia Sinica Information, 2019, 49(10): 1299-1320.
冀中, 汪浩然, 于云龙, 等. 零样本图像分类综述:十年进展[J]. 中国科学: 信息科学, 2019, 49(10): 1299-1320.
[74] XIAN Y Q, LORENZ T, SCHIELE B, et al. Feature generating networks for zero-shot learning[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-22, 2018. Piscataway: IEEE, 2018: 5542-5551.
[75] LIU Y, ZHU L, LIM K P, et al. Review and prospect of image super-resolution technology[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(2): 181-199.
刘颖, 朱丽, 林庆帆, 等. 图像超分辨率技术的回顾与展望[J]. 计算机科学与探索, 2020, 14(2): 181-199.
[76] LI X M, LI W Y, REN D W, et al. Enhanced blind face restoration with multi-exemplar images and adaptive spatial feature fusion[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, Jun 13-19, 2020. Piscataway: IEEE, 2020: 2703-2712.
[77] DING C X, TAO D C. A comprehensive survey on pose-invariant face recognition[J]. ACM Transactions on Intelligent Systems and Technology, 2016, 7(3): 1-42.
[78] TERH?RST P, KOLF J N, DAMER N, et al. SER-FIQ: unsupervised estimation of face image quality based on stochastic embedding robustness[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, Jun 13-19, 2020. Piscataway: IEEE, 2020: 5650-5659.
[79] WANG J Y, YANG H T, LI G Y, et al. Research progress of generative adversarial network and its application in image processing[J]. Computer Engineering and Applications, 2021, 57(8): 26-35.
王晋宇, 杨海涛, 李高源, 等. 生成对抗网络及其图像处理应用研究进展[J]. 计算机工程与应用, 2021, 57(8): 26-35.