Abstract
Deep generative models have been successfully applied to many applications. However, existing methods experience limitations when generating large images (the literature usually generates small images, e.g., \(32 \times 32\) or \(128 \times 128\)). In this paper, we propose a novel scheme using tensor super-resolution with adversarial generative nets (TSRGAN), to generate large high-quality images by exploring tensor structures. Essentially, the super resolution process of TSRGAN is based on tensor representation. First, we impose tensor structures for concise image representation, which is superior in capturing the pixel proximity information and the spatial patterns of elementary objects in images, over the vectorization preprocess in existing works. Secondly, we propose TSRGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions. More specifically, we design a tensor super-resolution process that consists of tensor dictionary learning and tensor coefficients learning. Finally, on three datasets, the proposed TSRGAN generates images with more realistic textures, compared with state-of-the-art adversarial autoencoders and super-resolution methods. The size of the generated images is increased by over 8.5 times, namely \(374\times 374\) in PASCAL2.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)
Denton, E.L., Chintala, S., Fergus, R., et al.: Deep generative image models using a laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems, pp. 1486–1494 (2015)
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)
Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Fei, J., Liu, X.-Y., Lu, H., Shen, R.: Efficient multi-dimensional tensor sparse coding using t-linear combinations. In: Association for the Advancement of Artificial Intelligence (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: Draw: a recurrent neural network for image generation. arXiv preprint arXiv:1502.04623 (2015)
Gu, S., Sang, N., Ma, F.: Fast image super resolution via local regression. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), pp. 3128–3131. IEEE (2012)
Hao, N., Kilmer, M.E., Braman, K., Hoover, R.C.: Facial recognition using tensor-tensor decompositions. SIAM J. Imaging Sci. 6(1), 437–463 (2013)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (2014)
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report. Citeseer (2009)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR, vol. 2, p. 4 (2017)
Liu, X.-Y., Aeron, S., Aggarwal, V., Wang, X.: Low-tubal-rank tensor completion using alternating minimization. arXiv preprint arXiv:1610.01690 (2017)
Liu, X.-Y., Aeron, S., Aggarwal, V., Wang, X., Wu, M.-Y.: Adaptive sampling of RF fingerprints for fine-grained indoor localization. IEEE Trans. Mob. Comput. 15(10), 2411–2423 (2015)
Liu, X.-Y., Wang, X.: Fourth-order tensors with multidimensional discrete transforms. arXiv preprint arXiv:1705.01576 (2017)
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders. In: International Conference on Learning Representations (2016)
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018)
Parikh, N., Boyd, S., et al.: Proximal algorithms. Found. Trends® Optim. 1(3), 127–239 (2014)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)
She, B., Wang, Y., Liang, J., Liu, Z., Song, C., Hu, G.: A data-driven avo inversion method via learned dictionaries and sparse representation. Geophysics 83(6), 1–91 (2018)
Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883 (2016)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853 (2015)
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)
Zhang, H., et al.: StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. In: IEEE International Conference on Computer Vision, pp. 5907–5915 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ding, Z., Liu, XY., Yin, M., Kong, L. (2019). Tensor Super-Resolution with Generative Adversarial Nets: A Large Image Generation Approach. In: Zeng, A., Pan, D., Hao, T., Zhang, D., Shi, Y., Song, X. (eds) Human Brain and Artificial Intelligence. HBAI 2019. Communications in Computer and Information Science, vol 1072. Springer, Singapore. https://doi.org/10.1007/978-981-15-1398-5_15
Download citation
DOI: https://doi.org/10.1007/978-981-15-1398-5_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1397-8
Online ISBN: 978-981-15-1398-5
eBook Packages: Computer ScienceComputer Science (R0)