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2018 | OriginalPaper | Chapter

Image Up-Sampling for Super Resolution with Generative Adversarial Network

Authors : Shohei Tsunekawa, Katsufumi Inoue, Michifumi Yoshioka

Published in: AI 2018: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

In case, we carry out single image Super Resolution (SR) utilizing deep learning, we utilize bicubic interpolation for up-sampling of low resolution images before input them into SR methods. In the preprocessing, these basic interpolation methods cause blur and noise effects for after processed images. These noise images may affect the SR results. In this research, by focusing on this point, we propose a new image up-sampling method utilizing Generative Adversarial Network (GAN). In this work, we improve an image evaluation criterion in generator part of GAN by combining Multi-Scale Structural Similarity (MS-SSIM) and L1 norm. From experiments, we have confirmed that our method allows us to create more qualitatively up-sampling images. As the quantitative results, our proposed method have achieved 0.90 [dB] of average PSNR, 3.35 [%] of average SSIM, and 1.28 [%] of average MS-SSIM improvement using Set 5 and Set 14 dataset compared with bicubic interpolation.

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previous chapter A Gaussian Filter-Based Feature Learning Approach Using Genetic Programming to Image Classification
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Literature
1.
Irani, M.: Improving resolution by image registration. CVGIP: Graph. Model. Image Process. 53, 231–239 (1991)
2.
Elad, M., Feuer, A.: Superresolution restoration of an image sequence: adaptive filtering approach. IEEE Trans. Image Process. 8(3), 387–395 (1999)CrossRef
3.
Cruz, C., Mehta, R., Katkovnik, V., Egiazarian, K.O.: Single image super-resolution based on wiener filter in similarity domain. CoRR abs/1704.04126 (2017)
4.
Wang, L., Xiang, S., Meng, G., Wu, H., Pan, C.: Edge-directed single-image super-resolution via adaptive gradient magnitude self-interpolation. IEEE Trans. Circuits Syst. Video Technol. 23(8), 1289–1299 (2013)CrossRef
5.
Fattal, R.: Upsampling via imposed edges statistics. ACM Transactions on Graphics 26(3) (2007). (Proceedings of SIGGRAPH 2007)
6.
Freeman, W., Jones, T., Pasztor, E.: Example-based super-resolution. IEEE Comput. Graph. Appl. 22(2), 56–65 (2002)CrossRef
7.
Sun, J., Zheng, N.N., Tao, H., Shum, H.Y.: Image hallucination with primal sketch priors. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings, vol. 2, pp. II-729-736 (2003)
8.
Dong, C., Loy, C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)CrossRef
9.
10.
Zhao, Y., et al.: GUN: gradual upsampling network for single image super-resolution. CoRR abs/1703.04244 (2017)
11.
Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680. Curran Associates, Inc. (2014)
12.
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. CoRR abs/1609.04802 (2016)
13.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
14.
Nguyen, A.M., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CoRR abs/1412.1897 (2014)
15.
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, vol. 2, pp. 1398–1402, November 2003
16.
Cai, L., Gao, H., Ji, S.: Multi-stage variational auto-encoders for coarse-to-fine image generation. CoRR abs/1705.07202 (2017)
17.
van den Oord, A., Kalchbrenner, N., Kavukcuoglu, K.: Pixel recurrent neural networks. CoRR abs/1601.06759 (2016)
18.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, June 2016
19.
Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging 3(1), 47–57 (2017)CrossRef
20.
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRef
21.
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, Upper Saddle River (2007)
22.
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. CoRR abs/1511.04587 (2015)
23.
Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. CoRR abs/1707.02921 (2017)
Metadata
Title
Image Up-Sampling for Super Resolution with Generative Adversarial Network
Authors
Shohei Tsunekawa
Katsufumi Inoue
Michifumi Yoshioka
Copyright Year
2018
Book
AI 2018: Advances in Artificial Intelligence

Print ISBN: 978-3-030-03990-5

Electronic ISBN: 978-3-030-03991-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-03991-2_26

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