الگوریتم¬های یادگیری عمیق در فراتفکیک پذیری تصاویر
الموضوعات :
1 -
2 - دانشکده مهندسی برق- واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
الکلمات المفتاحية: فراتفکیک پذیری, الگوریتم¬های یادگیری عمیق, تصویر با کیفیت, شبکه عصبی کانولوشن,
ملخص المقالة :
فراتفکیک¬پذیری تصویر یکی از فرآیند¬های مهم پردازش تصویر جهت افزایش وضوح تصاویر و ویدئو¬ها می¬باشد. در سال¬های اخیر، روش¬های مبتنی بر شبکه¬های عصبی عمیق جهت فراتفکیک¬پذیری شاهد پیشرفت قابل توجهی بوده است. هدف این مقاله ارائه یک بررسی جامع در مورد پیشرفت¬های اخیر فراتفکیک¬پذیری تصویر با استفاده از رویکرد¬های یادگیری عمیق است. در اين مقاله، ضمن معرفي مفاهیم فراتفکیک¬پذیری تصویر، به بررسی الگوریتم¬های رایج یادگیری عمیق جهت فراتفکیک¬پذیری، و کاربردهای فراتفکیک¬پذیری پرداخته شده¬است. علاوه بر این، مجموعه پایگاه¬های داده و معیارهای ارزیابی تشریح داده می¬شود. اين مقاله مي¬تواند راهگشاي محققان پردازش تصوير در فرآیند فراتفکیک¬پذیری باشد. اهتمام نويسندگان بر اين بوده است که همه جنبه¬هاي اين فرآیند مورد کاوش قرار گيرد.
المصادر:
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[1] F. Liu, X. Yang, and B. De Baets, "A deep recursive multi-scale feature fusion network for image super-resolution," Journal of Visual Communication and Image Representation, vol. 90, p. 103730, 2023.
[2] L. Inzerillo, F. Acuto, G. Di Mino, and M. Z. Uddin, "Super-resolution images methodology applied to UAV datasets to road pavement monitoring," Drones, vol. 6, p. 171, 2022.
[3] Y. Huang, L. Shao, and A. F. Frangi, "Simultaneous super-resolution and cross-modality synthesis of 3D medical images using weakly-supervised joint convolutional sparse coding," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 6070-6079.
[4] D. Yang, Z. Li, Y. Xia, and Z. Chen, "Remote sensing image super-resolution: Challenges and approaches," in 2015 IEEE international conference on digital signal processing (DSP), 2015, pp. 196-200.
[5] J. Jiang, C. Wang, X. Liu, and J. Ma, "Deep learning-based face super-resolution: A survey," ACM Computing Surveys (CSUR), vol. 55, pp. 1-36, 2021.
[6] A. B. Deshmukh and N. Usha Rani, "Fractional-Grey Wolf optimizer-based kernel weighted regression model for multi-view face video super resolution," International Journal of Machine Learning and Cybernetics, vol. 10, pp. 859-877, 2019.
[7] H. Liu, Z. Ruan, P. Zhao, C. Dong, F. Shang, Y. Liu, et al., "Video super-resolution based on deep learning: a comprehensive survey," Artificial Intelligence Review, vol. 55, pp. 5981-6035, 2022.
[8] S. Anwar, S. Khan, and N. Barnes, "A deep journey into super-resolution: A survey," ACM Computing Surveys (CSUR), vol. 53, pp. 1-34, 2020.
[9] C. Qiao, D. Li, Y. Liu, S. Zhang, K. Liu, C. Liu, et al., "Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes," Nature biotechnology, vol. 41, pp. 367-377, 2023.
[10] M. Chaika, S. Afat, D. Wessling, C. Afat, D. Nickel, S. Kannengiesser, et al., "Deep learning-based super-resolution gradient echo imaging of the pancreas: Improvement of image quality and reduction of acquisition time," Diagnostic and Interventional Imaging, vol. 104, pp. 53-59, 2023.
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[12] G. Liang, U. KinTak, H. Yin, J. Liu, and H. Luo, "Multi-scale hybrid attention graph convolution neural network for remote sensing images super-resolution," Signal Processing, vol. 207, p. 108954, 2023.
[13] W. Yang, X. Zhang, Y. Tian, W. Wang, J.-H. Xue, and Q. Liao, "Deep learning for single image super-resolution: A brief review," IEEE Transactions on Multimedia, vol. 21, pp. 3106-3121, 2019.
[14] Y. Li, B. Sixou, and F. Peyrin, "A review of the deep learning methods for medical images super resolution problems," Irbm, vol. 42, pp. 120-133, 2021.
[15] Y. LeCun, "LeNet-5, convolutional neural networks," URL: http://yann. lecun. com/exdb/lenet, vol. 20, p. 14, 2015.
[16] Y. Luo, L. Zhou, S. Wang, and Z. Wang, "Video satellite imagery super resolution via convolutional neural networks," IEEE Geoscience and Remote Sensing Letters, vol. 14, pp. 2398-2402, 2017.
[17] K. Umehara, J. Ota, and T. Ishida, "Application of super-resolution convolutional neural network for enhancing image resolution in chest CT," Journal of digital imaging, vol. 31, pp. 441-450, 2018.
[18] M. U. Müller, N. Ekhtiari, R. M. Almeida, and C. Rieke, "Super-resolution of multispectral satellite images using convolutional neural networks," arXiv preprint arXiv:2002.00580, 2020.
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[20] H. M. Keshk and X.-C. Yin, "Satellite super-resolution images depending on deep learning methods: a comparative study," in 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2017, pp. 1-7.
[21] C. Tian, R. Zhuge, Z. Wu, Y. Xu, W. Zuo, C. Chen, et al., "Lightweight image super-resolution with enhanced CNN," Knowledge-Based Systems, vol. 205, p. 106235, 2020.
[22] J. Yamanaka, S. Kuwashima, and T. Kurita, "Fast and accurate image super resolution by deep CNN with skip connection and network in network," in Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part II 24, 2017, pp. 217-225.
[23] C. Dong, C. C. Loy, K. He, and X. Tang, "Learning a deep convolutional network for image super-resolution," in European conference on computer vision, 2014, pp. 184-199.
[24] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., "Generative adversarial nets," Advances in neural information processing systems, vol. 27, 2014.
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[26] Y. Gu, Z. Zeng, H. Chen, J. Wei, Y. Zhang, B. Chen, et al., "MedSRGAN: medical images super-resolution using generative adversarial networks," Multimedia Tools and Applications, vol. 79, pp. 21815-21840, 2020.
[27] H. Liu, J. Liu, S. Hou, T. Tao, and J. Han, "Perception consistency ultrasound image super-resolution via self-supervised CycleGAN," Neural Computing and Applications, pp. 1-11, 2021.
[28] D. Mahapatra, B. Bozorgtabar, and R. Garnavi, "Image super-resolution using progressive generative adversarial networks for medical image analysis," Computerized Medical Imaging and Graphics, vol. 71, pp. 30-39, 2019.
[29] X. Bing, W. Zhang, L. Zheng, and Y. Zhang, "Medical image super resolution using improved generative adversarial networks," IEEE Access, vol. 7, pp. 145030-145038, 2019.
[30] X. Yu and F. Porikli, "Ultra-resolving face images by discriminative generative networks," in European conference on computer vision, 2016, pp. 318-333.
[31] L. R. Medsker and L. Jain, "Recurrent neural networks," Design and Applications, vol. 5, p. 2, 2001.
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[33] W. Weng, Y. Zhang, and Z. Xiong, "Boosting event stream super-resolution with a recurrent neural network," in European Conference on Computer Vision, 2022, pp. 470-488.
[34] W. Han, S. Chang, D. Liu, M. Yu, M. Witbrock, and T. S. Huang, "Image super-resolution via dual-state recurrent networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 1654-1663.
[35] Y. Fu, Z. Liang, and S. You, "Bidirectional 3D quasi-recurrent neural network for hyperspectral image super-resolution," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 2674-2688, 2021.
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[40] K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, "LSTM: A search space odyssey," IEEE transactions on neural networks and learning systems, vol. 28, pp. 2222-2232, 2016.
[41] Y. Chang and B. Luo, "Bidirectional convolutional LSTM neural network for remote sensing image super-resolution," Remote Sensing, vol. 11, p. 2333, 2019.
[42] H. Zhu, M. Guo, H. Li, Q. Wang, and A. Robles-Kelly, "Breaking the spatio-angular trade-off for light field super-resolution via lstm modelling on epipolar plane images," arXiv preprint arXiv:1902.05672, 2019.
[43] C. Chou, J. Park, and E. Chou, "Generating high-resolution climate change projections using super-resolution convolutional LSTM neural networks," in 2021 13th International Conference on Advanced Computational Intelligence (ICACI), 2021, pp. 293-298.
[44] X. Lu, X. Liu, L. Zhang, F. Jia, and Y. Yang, "Hyperspectral image super-resolution based on attention ConvBiLSTM network," International Journal of Remote Sensing, vol. 43, pp. 5059-5074, 2022.
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