Research of Super-resolution Processing of Invoice Image Based on Generative Adversarial Network

doi:10.16182/j.issn1004731x.joss.20-0095

Abstract

Abstract: Automatic identification of invoices can effectively improve financial efficiency. But low-resolution invoice image reduces the accuracy of automatic identification, an ESRGAN (Encoder Super-resolution Generative Adversarial Network) network for super-resolution processing of invoice images is proposed. The ESRGAN network is based on a conditional generative adversarial network. An auxiliary encoder is designed to guide the network to generate a more realistic super-resolution image. Based on the actual invoice image, the ESRGAN network and the conventional image processing, SRCNN (Super-resolution Convolutional Neural Networks) network and SRGAN (Super-resolution Generative Adversarial Network) network. The model is evaluated through two evaluation indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The experimental results show that the images processed based on ESRGAN super-resolution are better on visual effects and evaluation indicators.

Key words: invoice image, super-resolution, generative adversarial networks, ESRGAN, evaluation indicator

CLC Number:

TP391.4

Li Xinli, Zou Changming, Yang Guotian, Liu He. Research of Super-resolution Processing of Invoice Image Based on Generative Adversarial Network[J]. Journal of System Simulation, 2021, 33(6): 1307-1314.

References

[1] Rafael C G, Richard E W.数字图像处理[M]. 3版. 北京: 电子工业出版社, 2014: 97-101.
Rafael C G, Richard E W.Digital Image Processing [M]. 3rd ed. Beijing: Publishing House of Electronic Industry, 2014: 97-101.
[2] Chao D, Chen C L, He K M, et al.Learning a Deep Convolutional Network for Image Super-Resolution[C]// European Conference on Computer Vision (ECCV). Switzerland: Springer International Publishing, 2014: 184-199.
[3] 范明明, 池源, 张铭津, 等. 基于深度学习的芯片图像超分辨率重建[J]. 模式识别与人工智能, 2019, 32(4): 353-360.
Fan Mingming, Chi Yuan, Zhang Mingjin, et al.Super-resolution Reconstruction of Chip Images Based on Deep Learning[J]. Pattern Recognition and Artificial Intelligence, 2019, 32(4): 353-360.
[4] Goodfellow I J, Poutget-Abadie J, Mirza M, et al.Generative Adversarial Nets[C]// The 27th International Conference on Neural Information Processing Systems. Montreal. CANADA: NIPS, 2014:2672-2680.
[5] Rezende D J, Mohamed S, Wierstra D.Stochastic Back-propagation and Approximate Inference in Deep Generative Models[C]// International Conference on Machine Learning (ICML). Beijing: ICML, 2014: 1-9.
[6] Hinton G E, Osindero S, Teh Y W.A Fast Learning Algorithm for Deep Belief Nets[J]. Neural Computation (S0899-7667), 2006, 18(7): 1527-1554.
[7] 王坤峰, 苟超, 段艳杰, 等. 生成式对抗网络 GAN 的研究进展与展望[J]. 自动化学报, 2017, 43(3): 321-332.
Wang Kunfeng, Gou Chao, Duan Yanjie, et al.Research Progress and Prospect of Generative Adversarial Networks[J]. Acta Automatica Sinica, 2017, 43(3): 321-332.
[8] 林懿伦, 戴星原, 李力, 等. 人工智能研究的新前线:生成式对抗网络[J]. 自动化学报, 2018, 44(5): 775-792.
Lin Yilun, Dai Xingyuan, Li Li, et al.A New Frontier for Artificial Intelligence Research: Generative Adversarial Networks[J]. Acta Automatica Sinica, 2018, 44(5): 775-792.
[9] Ledig C, Theis L, Huszar F, et al.Photo-realistic Single Image Super-resolution Using a Generative Adversarial Network[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017: 105-114.
[10] Isola P, Zhu J Y, Zhou T, et al.Image-to-Image Translation with Conditional Adversarial Networks[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017: 5967-5976.
[11] Zhu J Y, Park T, Isola P, et al.Unpaired Image-to-Image Translation Using Cycle-consistent Adversarial Networks[C]// IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE, 2017: 2242-2251.
[12] Choi Y, Choi M, Kim M, et al.StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT, USA: IEEE, 2018: 8789-8797.
[13] He Z L, Zuo W M, Kan M N, et al.AttGAN: Facial Attribute Editing by Only Changing What You Want[J]. IEEE Transactions on Image Processing (S1057-7149), 2019, 28(11): 5464-5478.
[14] Qina R, Tan R T, Yang W H, et al.Attentive Generative Adversarial Network for Raindrop Removal From a Single Image[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT, USA: IEEE, 2018: 2482-2491.
[15] Mirza M, Osindero S.Conditional Generative Adversarial nets[C]// The 18th Annual Conference of the International Speech Communication Association (INTERSPEECH). Stockholm, Sweden: ISCA, 2017: 2814-2818.
[16] Wang H, Wang Y T, Zhou Z, et al.CosFace: Large Margin Cosine Loss for Deep Face Recognition[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT, USA: IEEE, 2018: 5265-5274.
[17] Wang Y J, Li J H, Lu Yi, et al.Image Quality Evaluation Based on Image Weighted Separating Block Peak Signal to Noise Ratio[C]// 2003 International Conference on Neural Networks and Signal Processing (ICNNSP'03). Nanjing: IEEE, 2003: 994-997.
[18] Wang Z, Bovik A C, Sheikh H R, et al.Image Quality Assessment: from Error Visibility to Structural Similarity[J]. IEEE Transactions on Image Processing (S1057-7149), 2004, 13(4): 600-612.