그림 1. SRCNN 모델 구조 Fig. 1. SRCNN model structure
그림 2. 잔차 학습의 구조 Fig. 2. The structure of residual learning
그림 3. 재귀 잔차 블록의 구조. U는 재귀 블록 내 잔차 단위 개수이다. Fig. 3. Structures of our recursive residual block. U means number of residual units in the recursive block
그림 4. 본 논문에서 제안된 CNN 모델의 구조 Fig. 4. The architecture of our proposed CNN model
그림 5. Set5의 “butterfly” 영상의 2배 확대 실험 결과 Fig. 5. The result image of “butterfly” from Set5 with an upscaling factor=2
그림 6. Set14의 “zebra” 영상의 2배 확대 실험 결과 Fig. 6. The result image of “zebra” from Set14 with an upscaling factor=2
표 1. Set5, Set14의 2배, 3배, 4배 확대 결과 평균 PSNR Table 1. Average PSNR for scale 2, 3, and 4 on datasets Set5, and Set14
참고문헌
- W. T. Freeman, E. C. Pasztor, and O. T. Carmichael, "Learning low-level vision," International Journal of Computer Vision, Vol.40, No.11, pp. 25-47. 2000. https://doi.org/10.1023/A:1026501619075
- D. Glasner, S. Bagon, and M. Irani, "Super-resolution from a single image," Proceeding of International Conference on Computer Vision, 2009.
- M. Irani, and S. Peleg, "Improving resolution by image registration," Graphical models and Image Processing, pp.231-239, 1991.
- S. Peled, and Y. L. Cun, "Super-resolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging," Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , Vol.45, No.1, pp. 29-35, 2001. https://doi.org/10.1002/1522-2594(200101)45:1<29::AID-MRM1005>3.0.CO;2-Z
- W. Shi, J. Caballero, C. Ledig, X. Zhuang, W. Bai, K. Bhatia, A. Marvao, T. Dawes, D. ORegan, and D. Rueckert, "Cardiac image super-resolution with global correspondence using multi-atlas patchmatch," Proceeding of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.9-16. 2013.
- W. Zou and P. C. Yuen, "Very low resolution face recognition problem," IEEE Transaction on Image Processing, Vol.21, No.1, pp.327-340, July 2011. https://doi.org/10.1109/TIP.2011.2162423
- B. K. Gunturk, A. U. Batur, Y. Altunbasak, M. H. Hayes, and R. M. Mersereau, "Eigenface-domain super-resolution for face recognition," IEEE Transaction on Image Processing, Vol.12, No.5, pp.597-606, 2003. https://doi.org/10.1109/TIP.2003.811513
- D. Y. Han, "Comparison of commonly used image interpolation methods," Proceeding of International Conference on Computer Science and Electronics Engineering, Mar 2013.
- C. E. Duchon, "Lanczos filtering in one and two dimensions," Journal of Applied Meteorology, pp.1016-1022, 1979.
- H. Chang, D. Y. Yeung, and Y. Xiong, "Super-resolution through neighbor embedding," Proceeding of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2004.
- C. G. Marco Bevilacqua, Aline Roumy, and M. L. A. Morel, "Low complecity single-image super-resolution based on nonnegative neighbor embedding," Proceeding of IEEE British Machine Vision Conference, 2012.
- R. Timofte, V. De Smet, and L. Van Gool, "A+: Adjusted anchored neighborhood regression for fast super-resolution," Proceeding of Asian Conference on Computer Vision (ACCV), Nov 2014.
- J. Yang, J. Wright, T. S. Huang, and Y. Ma, "Image super-resolution via sparse representation," IEEE Transaction on Image Processing, Vol.19, No.11, Nov 2010.
- C. Dong, C. C. Loy, K. He, and X. Tang, "Image super-resolution using deep convolution networks," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol.38, No.2, Feb 2016.
- C. Dong, C. C. Loy, K. He, and X. Tang, "Accelerating the super-resolution convolutional neural network." Proceeding of European Conference on Computer Vision (ECCV), 2016.
- J. Kim, J. Kwon, and K. Mu. Lee, "Accurate image super-resolution using very deep convolution networks," Proceeding of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2016.
- A. Krizhevsky, I. Sutskever, and G. Hinton, "Imagenet calssification with deep convolutiuon neural network," Neural Information Processing Systems (NIPS), 2012.
- P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. L. Cun, "Overfeat: Intergrated recognition, localization and detection using convolution networks," Proceeding of IEEE conference on Computer Vision and Pattern Recognition (CVPR), Dec 2013.
- K. He, X. Y. Zhang, S. Q. Ren, and J. S, "Deep residual learning for image recognition," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Dec 2015.
- G. H, Z. L, L. van der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," Proceeding of IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2017.
- S. Ioffe, and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," Procceding of International Conference on Machine Learning(ICML), pp 1097-1105, 2012.
- Nah, T. H. Kim, and K. M. Lee, "Deep multi-scale convolutional neural network for dynamic scene deblurring," Proceeding of IEEE conference on Computer Vision and Pattern Recognition (CVPR), Dec. 2016.
- R. Timofte, R. Rothe, and L. Cool, "Seven ways to improve example-based single image super resolution," Proceeding of IEEE conference on Computer Vision and Pattern Recognition (CVPR), pp.1865-1873, Jun 2016.
- D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," Proceeding of IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2001.
- R. Zeyde, M. Elad, and M. Protter, "On single image scale-up using sparse-representations," Proceeding of International Conference on Curves and Surfaces. pp.711-730, 2012.