Browse > Article

Quantized CNN-based Super-Resolution Method for Compressed Image Reconstruction  

Kim, Yongwoo (Department of System Semiconductor Engineering, Sangmyung University)
Lee, Jonghwan (Department of System Semiconductor Engineering, Sangmyung University)
Publication Information
Journal of the Semiconductor & Display Technology / v.19, no.4, 2020 , pp. 71-76 More about this Journal
Abstract
In this paper, we propose a super-resolution method that reconstructs compressed low-resolution images into high-resolution images. We propose a CNN model with a small number of parameters, and even if quantization is applied to the proposed model, super-resolution can be implemented without deteriorating the image quality. To further improve the quality of the compressed low-resolution image, a new degradation model was proposed instead of the existing bicubic degradation model. The proposed degradation model is used only in the training process and can be applied by changing only the parameter values to the original CNN model. In the super-resolution image applying the proposed degradation model, visual artifacts caused by image compression were effectively removed. As a result, our proposed method generates higher PSNR values at compressed images and shows better visual quality, compared to conventional CNN-based SR methods.
Keywords
Super-Resolution; CNN; Deep Learning; Degradation Model; Compressed Image;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 M. Bevilacqua et al., "Low-complexity single-image super-resolution based on nonnegative neighbor embedding," Proc. 23rd Brit. Mach. Vis. Conf. (BMVC), Guildford, U.K., Sep. 2012, pp. 135.1-135.10.
2 R. Zeyde, M. Elad, and M. Protter, "On single image scale-up using sparse-representations," Proc. 7th Int. Conf. Curves Surf., Jun. 2010, pp. 711-730.
3 J.-B. Huang, A. Singh, and N. Ahuja, "Single image super-resolution from transformed self-exemplars," Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Boston, MA, USA, Jun. 2015, pp. 5197-5206.
4 E. Perez-Pellitero, J. Salvador, J. Ruiz-Hidalgo, and B. Rosenhahn, "Accelerating super-resolution for 4K upscaling," in Proc. IEEE. Int. Conf. Consum. Electron., Las Vegas, NV, USA, Jan. 2015, pp. 317-320.
5 Y. Lee and Y.H. Lee, "Implementation of Moving Object Recognition based on Deep Learning," Journal of the Semiconductor & Display Technology, Vol. 17, No. 2, pp.67-70, 2018.
6 Y. C and Y. C, "A Deep Learning based IOT Device Recognition System," Journal of the Semiconductor & Display Technology, Vol. 18, pp. 1-5, 2019.
7 C. Dong, C. C. Loy, K. He, and X. Tang, "Learning a deep convolutional network for image super-resolution," Proc. Eur. Conf. Comput. Vis., Zurich, Switzerland, Sep. 2014, pp. 184-199.
8 C. Dong, C. C. Loy, K. He, and X. Tang, "Image superresolution using deep convolutional networks," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 295-307, Feb. 2015.   DOI
9 C. Dong, C. C. Loy, and X. Tang, "Accelerating the Super-Resolution Convolutional Neural Network," Part II Proc. European Conf. Computer Vision (ECCV), Springer, LNCS 9906, pp. 391-407, Amsterdam, Netherland, Oct. 8-16, 2016.
10 A. Aitken, C. Ledig, L. Thesis, J. Caballero, Z. Wang, and W. Shi. (2017). "Checkerboard artifact free subpixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize." [Online]. Available: https://arxiv.org/abs/1707.02937.
11 J. Kim, J. Kwon. Lee and K. Mu Lee, "Accurate Image Super-Resolution Using Very Deep Convolutional Networks," Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646 - 1654, Las Vegas, USA, June 27-30, 2016.
12 Kai Zhang, Wangmeng Zuo, and Lei Zhang, "Learning a single convolutional super-resolution network for multiple degradations," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 3262-3271.
13 K. Simonyan, A. Zisserman. (2015). "Very Deep Convolutional Networks for Large-Scale Image Recognition." [Online]. Available: https://arxiv.org/abs/1409.1556.
14 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 subpixel convolutional neural network," Proc. IEEE Conf. Comput. Vision and Pattern Recog. (CVPR), Las Vegas, USA, June 27-30, 2016, pp. 1874-1883.
15 B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee, "Enhanced Deep Residual Networks for Single Image Super-Resolution," Proc. IEEE Conf. Comput. Vision and Pattern Recog. Workshops, Hawaii, USA, July 21- 26, 2017, pp. 1132-1140.
16 Y. Kim, J. Choi and M. Kim, "A Real-Time Convolutional Neural Network for Super-Resolution on FPGA With Applications to 4K UHD 60 fps Video Services," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 8, pp. 2521-2534, Aug. 2019.   DOI
17 J. Yang, J. Wright, T. S. Huang, and Y. Ma, "Image super-resolution via sparse representation," IEEE Trans. Image Process., vol. 19, no. 11, pp. 2861-2873, Nov. 2010.   DOI
18 M. Shabany, "Floating-point to Fixed-point conversion." [Online]. Available: http://ee.sharif.edu/-digitalvlsi/Docs/Fixed-Point.pdf.
19 S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan. (2015). "Deep Learning with Limited Numerical Precision." [Online]. Available: https://arxiv.org/abs/1502.02551.
20 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," Proc. 8th IEEE Int. Conf. Comput. Vis., vol. 2. Jul. 2001, pp. 416-423.