Skip to main content
SpringerLink
Log in

Deep Learning-Based Upscaling for In Situ Volume Visualization

  • Conference paper
  • First Online:
In Situ Visualization for Computational Science

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Complementary to the classical use of feature-based condensation and temporal subsampling for in situ visualization, learning-based data upscaling has recently emerged as an interesting approach that can supplement existing in situ volume visualization techniques. By upscaling we mean the spatial or temporal reconstruction of a signal from a reduced representation that requires less memory to store and sometimes even less time to generate. The concrete tasks where upscaling has been shown to work effectively are geometry upscaling, to infer high-resolution geometry images from given low-resolution images of sampled features; upscaling in the data domain, to infer the original spatial resolution of a 3D dataset from a downscaled version; and upscaling of temporally sparse volume sequences, to generate refined temporal features. In this book chapter, we aim at providing a summary of existing learning-based upscaling approaches and a discussion of possible use cases for in situ volume visualization. We discuss the basic foundation of learning-based upscaling, and review existing works in image and video super-resolution from other fields. We then show the specific adaptations and extensions that have been proposed in visualization to realize upscaling tasks beyond color images, discuss how these approaches can be employed for in situ visualization, and provide an outlook on future developments in the field.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bai, K., Li, W., Desbrun, M., Liu, X.: Dynamic upsampling of smoke through dictionary-based learning (2019). arXiv:1910.09166

  2. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Lear. 2(1), 1–127 (2009)

    Article  MathSciNet  Google Scholar 

  3. Caballero, J., Ledig, C., Aitken, A.P., Acosta, A., Totz, J., Wang, Z., Shi, W.: Real-time video super-resolution with spatio-temporal networks and motion compensation (2016). arXiv:1611.05250

  4. Chaitanya, C.R.A., Kaplanyan, A.S., Schied, C., Salvi, M., Lefohn, A., Nowrouzezahrai, D., Aila, T.: Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder. ACM Trans. Graph. 36(4), 98:1–98:12 (2017)

    Google Scholar 

  5. Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014). arXiv:1406.1078

  6. Chu, M., Thuerey, N.: Data-driven synthesis of smoke flows with CNN-based feature descriptors. ACM Trans. Graph. 36(4), 69:1–69:14 (2017)

    Google Scholar 

  7. Chu, M., Xie, Y., Leal-Taixé, L., Thuerey, N.: Temporally coherent gans for video super-resolution (TecoGAN) (2018). arXiv:1811.09393

  8. Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Proceedings of European Conference on Computer Vision, pp. 184–199 (2014)

    Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. In: Proceedings of Annual Conference on Neural Information Processing Systems, pp. 658–666 (2016)

    Google Scholar 

  11. Eckert, M.-L., Um, K., Thuerey, N.: ScalarFlow: a large-scale volumetric data set of real-world scalar transport flows for computer animation and machine learning. ACM Trans. Graph. 38(6), 239:1–239:16 (2019)

    Google Scholar 

  12. Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)

    Google Scholar 

  13. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  14. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Proceedings of Annual Conference on Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  15. Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for improved unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)

    Article  Google Scholar 

  16. Guo, L., Ye, S., Han, J., Zheng, H., Gao, H., Chen, D.Z., Wang, J.-X., Wang, C.: SSR-VFD: Spatial super-resolution for vector field data analysis and visualization. In: Proceedings of IEEE Pacific Visualization Symposium, pp. 71–80 (2020)

    Google Scholar 

  17. Han, J., Tao, J., Zheng, H., Guo, H., Chen, D.Z., Wang, C.: Flow field reduction via reconstructing vector data from 3D streamlines using deep learning. IEEE Comput. Graph. Appl. 39(4), 54–67 (2019)

    Article  Google Scholar 

  18. Han, J., Wang, C.: TSR-TVD: temporal super-resolution for time-varying data analysis and visualization. IEEE Trans. Vis. Comput. Graph. 26(1), 205–215 (2020)

    Google Scholar 

  19. Han, J., Wang, C.: SSR-TVD: spatial super-resolution for time-varying data analysis and visualization. IEEE Trans. Vis. Comput. Graph. (Under Minor Revision) (2020)

    Google Scholar 

  20. Han, J., Zheng, H., Xing, Y., Chen, D.Z., Wang, C.: V2V: a deep learning approach to variable-to-variable selection and translation for multivariate time-varying data. IEEE Trans. Vis. Comput. Graph. 27(2) (2021). In Press

    Google Scholar 

  21. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  22. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  23. Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)

    Google Scholar 

  24. Jo, Y., Oh, S.W., Kang, J., Kim, S.J.: Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3224–3232 (2018)

    Google Scholar 

  25. Johnson, J., Alahi, A., Li, F.-F.: Perceptual losses for real-time style transfer and super-resolution. In: Proceedings of European Conference on Computer Vision, pp. 694–711 (2016)

    Google Scholar 

  26. Kappeler, A., Yoo, S., Dai, Q., Katsaggelos, A.K.: Video super-resolution with convolutional neural networks. IEEE Trans. Comput. Imaging 2(2), 109–122 (2016)

    Article  MathSciNet  Google Scholar 

  27. Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637–1645 (2016)

    Google Scholar 

  28. Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)

    Google Scholar 

  29. Kramer, M.A.: Nonlinear principal component analysis using autoassociative neural networks. AIChE J. 37(2), 233–243 (1991)

    Article  Google Scholar 

  30. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  31. Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep Laplacian pyramid networks for fast and accurate superresolution. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 624–632 (2017)

    Google Scholar 

  32. LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  33. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A.P., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

    Google Scholar 

  34. Liu, D., Wang, Z., Fan, Y., Liu, X., Wang, Z., Chang, S., Huang, T.: Robust video super-resolution with learned temporal dynamics. In: Proceedings of International Conference on Computer Vision, pp. 2526–2534 (2017)

    Google Scholar 

  35. Mao, X., Li, Q., Xie, H., Lau, R.Y.K., Wang, Z., Smolley, S.P.: Least squares generative adversarial networks. In: Proceedings of International Conference on Computer Vision, pp. 2813–2821 (2017)

    Google Scholar 

  36. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: representing scenes as neural radiance fields for view synthesis (2020). arXiv:2003.08934

  37. Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: Proceedings of International Conference for Learning Representations (2018)

    Google Scholar 

  38. Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of International Conference on Machine Learning, pp. 807–814 (2010)

    Google Scholar 

  39. Nimier-David, M., Vicini, D., Zeltner, T., Wenzel, J.: Mitsuba 2: a retargetable forward and inverse renderer. ACM Trans. Graph. 38(6), 203:1–203:17 (2019)

    Google Scholar 

  40. Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: Feature learning by inpainting. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)

    Google Scholar 

  41. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241 (2015)

    Google Scholar 

  42. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  Google Scholar 

  43. Sajjadi, M.S.M., Schólkopf, B., Hirsch, M.: EnhanceNet: single image super-resolution through automated texture synthesis. In: Proceedings of IEEE International Conference on Computer Vision, pp. 4501–4510 (2017)

    Google Scholar 

  44. Sajjadi, M.S.M., Vemulapalli, R., Brown, M.: Frame-recurrent video super-resolution. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 6626–6634 (2018)

    Google Scholar 

  45. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  46. Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., Woo, W.-C.: Convolutional LSTM network: a machine learning approach for precipitation Nowcasting. In: Proceedings of Advances in Neural Information Processing Systems, pp. 802–810 (2015)

    Google Scholar 

  47. Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883 (2016)

    Google Scholar 

  48. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556

  49. Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3147–3155 (2017)

    Google Scholar 

  50. Tao, X., Gao, H., Liao, R., Wang, J., Jia, J.: Detail-revealing deep video super-resolution. In: Proceedings of IEEE International Conference on Computer Vision, pp. 4482–4490 (2017)

    Google Scholar 

  51. Tong, T., Li, G., Liu, X., Gao, Q.: Image super-resolution using dense skip connections. In: Proceedings of IEEE International Conference on Computer Vision, pp. 4809–4817 (2017)

    Google Scholar 

  52. Wang, X., Chan, K.C., Yu, K., Dong, C., Loy, C.C.: EDVR: Video restoration with enhanced deformable convolutional networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  53. Weiss, S., Chu, M., Thuerey, N., Westermann, R.: Volumetric isosurface rendering with deep learning-based super-resolution. IEEE Trans. Vis. Comput. Graph. (Accepted) (2019)

    Google Scholar 

  54. Weiss, S., Işık, M., Thies, J., Westermann, R.: Learning adaptive sampling and reconstruction for volume visualization (2020). arXiv:2007.10093

  55. Werhahn, M., Xie, Y., Chu, M., Thuerey, N.: A multi-pass GAN for fluid flow super-resolution. Proc. ACM Comput. Graph. Inter. Tech. 2(1), 10:1–10:21 (2019)

    Google Scholar 

  56. Xiao, X., Wang, H., Yang, X.: A CNN-based flow correction method for fast preview. Comput. Graph. Forum 38(2), 431–440 (2019)

    Article  Google Scholar 

  57. Xie, Y., Franz, E., Chu, M., Thuerey, N.: tempoGAN: a temporally coherent, volumetric GAN for super-resolution fluid flow. ACM Trans. Graph. 37(4), 95:1–95:15 (2018)

    Google Scholar 

  58. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising (2016). arXiv:1608.03981

  59. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018)

    Google Scholar 

  60. Zhou, Z., Hou, Y., Wang, Q., Chen, G., Lu, J., Tao, Y., Lin, H.: Volume upscaling with convolutional neural networks. In: Proceedings of Computer Graphics International, pp. 38:1–38:6 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Technical University of Munich, Garching bei München, Germany

    Sebastian Weiss & Rüdiger Westermann

  2. University of Notre Dame, Notre Dame, IN, USA

    Jun Han & Chaoli Wang

Authors
  1. Sebastian Weiss
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaoli Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rüdiger Westermann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rüdiger Westermann .

Editor information

Editors and Affiliations

  1. Computer and Information Science, University of Oregon, Eugene, OR, USA

    Hank Childs

  2. Extreme-Scale Data Science and Analytics, Sandia National Laboratories, Livermore, CA, USA

    Janine C. Bennett

  3. Scientific Visualization Lab, University of Kaiserslautern, Kaiserslautern, Germany

    Christoph Garth

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Weiss, S., Han, J., Wang, C., Westermann, R. (2022). Deep Learning-Based Upscaling for In Situ Volume Visualization. In: Childs, H., Bennett, J.C., Garth, C. (eds) In Situ Visualization for Computational Science. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-81627-8_15

Download citation

Publish with us

Policies and ethics

Search

Navigation