Acknowledgement
This work was supported by Institute of Information & Communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No.2020-0-00004, Development of Previsional Intelligence based on Long-term Visual Memory Network)
References
- C. Dong, C. C. Loy, K. He, and X. Tang, "Image super-resolution using deep convolutional networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 295-307, Feb. 2016. https://doi.org/10.1109/TPAMI.2015.2439281
- W. T. Freeman, T. R. Jones, and E. C. Pasztor, "Example-bas ed super-resolution," IEEE Computer Graphics and Applications, vol. 22, no. 2, pp. 56-65, Mar-Apr. 2002. https://doi.org/10.1109/38.988747
- J. Yang, J. Wright, T. Huang, and Y. Ma, "Image super-resolution as sparse representation of raw image patches," in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, pp. 1-8, 2008.
- C. Dong, C. C. Loy, and X. Tang, "Accelerating the super-resolution convolutional neural network," in Proceeding of the European Conference on Computer Vision, Springer, Cham. pp. 391-407, Aug. 2016.
- W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, "Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network," in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874-1883, Sep. 2016.
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, Jun-Jul. 2016.
- B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, "Enhanced Deep Residual Networks for Single Image Super-Resolution," in Proceeding of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, pp. 1132-1140, 2017.
- K. Simonyan and A. Zisserman. "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
- J. W. Kim, J. K. Lee, and K. M. Lee, "Accurate image super-resolution using very deep convolutional networks," in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646-1654, 2016.
- G. Huang, Z. Liu, L. Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in Proceeding of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, pp. 2261-2269, 2017.
- M. Haris, G. Shakhnarovich, and N. Ukita, "Deep back-projection networks for super-resolution," in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1664-1673, Mar. 2018.
- Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, "Residual dense network for image super-resolution," in Proceeding of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2472-2481, Mar. 2018.
- Y. Guo, J. Chen, J. Wang, Q. Chen, J. Cao, Z. Deng, Y. Xu, and M. Tan, "Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution," in Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5407-5416, May. 2020.
- K. Zhang, L. V. Gool, and R. Timofte, "Deep unfolding network for image super-resolution," in Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3217-3226, Mar. 2020.
- J. W. Soh, S. Cho, and N. I. Cho, "Meta-Transfer Learning for Zero-Shot Super-Resolution," in Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3516-3525, Feb. 2020.
- J. H. Lee, S. M. Cho, S. J. Lee, and C. H. Kim, "License Plate Recognition System Using Synthetic Data," Journal of the Institute of Electronics and Information Engineers, vol. 57, no. 1, pp. 107-115, Jan. 2020. https://doi.org/10.5573/ieie.2020.57.1.107
- Y. J. Lee, S. J. Kim, K. M. Park, and K. M. Park, "Comparison of number plate recognition performance of Synthetic number plate generator using 2D and 3D rotation," The Korean Institute of Broadcast and Media Engineers Summer Conference, pp. 141-144, Jul. 2020.
- Z. Sergey and G. Alexey, "LPRNet: License Plate Recognition via Deep Neural Networks," arXiv preprint arXiv:1806.10447, Jun. 2018.
- W. Liu, D. Anguelov, D. Derhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, "SSD: Single Shot MultiBox Detector," in Proceeding of the European Conference on Computer Vision, pp. 21-37, Dec. 2016.
- Z. Xu, W. Yang, A. Meng, N. Lu, H. Huang, C. Ying, and L. Huang, "Towards end-to-end license plate detection and recognition: A large dataset and baseline," in Proceeding of the European Conference on Computer Vision, pp. 261-277, Sep. 2018.
- H. C. Lee, "Design and Implementation of Efficient Place Number Region Detecting System in Vehicle Number Plate Image," Korea Society Of Computer Information Journal, vol. 10, no. 5, pp. 87-93, Nov. 2005.
- Translate darknet to tensorflow [Internet]. Available: https://github.com/thtrieu/darkflow.
- S. B. Yoo and M. Han, "Temporal matching prior network for vehicle license plate detection and recognition in videos," ETRI Journal, vol. 42, no. 3, pp. 411-419, Feb. 2020. https://doi.org/10.4218/etrij.2019-0245
- J. H. Baek and N. H. Kim, "Noise Removal Method using Entropy in High-Density Noise Environments," Journal of the Korea Institute of Information and Communication Engineering, vol. 24, no. 10, pp. 1255-1261, Oct. 2020. https://doi.org/10.6109/JKIICE.2020.24.10.1255
- D. J. Kim and P. L. Manjusha, "Building Detection in High Resolution Remotely Sensed Images based on Automatic Histogram-Based Fuzzy C-Means Algorithm," Asia-pacific Journal of Convergent Research Interchange, vol. 3, no. 1, pp. 57-62, Mar. 2017. https://doi.org/10.21742/apjcri.2017.12.11