Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Underwater Light Field Depth Map Restoration Using Deep Convolutional Neural Fields

Authors : Huimin Lu, Yujie Li, Hyoungseop Kim, Seiichi Serikawa

Published in: Artificial Intelligence and Robotics

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Underwater optical images are usually influenced by low lighting, high turbidity scattering and wavelength absorption. In order to solve these issues, a great deal of work has been used to improve the quality of underwater images. Most of them used the high-intensity LED for lighting to obtain the high contrast images. However, in high turbidity water, high-intensity LED causes strong scattering and absorption. In this paper, we firstly propose a light field imaging approach for solving underwater depth map estimation problems in low-intensity lighting environment. As a solution, we tackle the problem of de-scattering from light field images by using deep convolutional neural fields in depth estimation. Experimental results show the effectiveness of the proposed method through challenging real world underwater imaging.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Motor Anomaly Detection for Aerial Unmanned Vehicles Using Temperature Sensor
next chapter Image Processing Based on the Optimal Threshold for Signature Verification
Literature
1.
Ligten, R.: Influence of photographic film on wavefront reconstruction. J. Opt. Soc. Am. 56, 1009–1014 (1966)CrossRef
2.
3.
4.
5.
Dansereau, D., Bongiorno, D., Pizarro, O., Williams, S.: Light field image denoising using a linear 4D frequency-hyperfan all-in-focus filter. In: Proceedings SPIE Computational Imaging XI, Feb 2013
6.
Wilburn, B., Joshi, N., Vaish, V., Talvala, E., Antunez, E., Barth, A., Adams, A., Horowitz, M., Levoy, M.: High performance imaging using large camera arrays. ACM Trans. Graphics 24(3), 765–776 (2005)CrossRef
7.
Veeraraghavan, A., Raskar, R., Agrawal, A., Mohan, A., Tumblin, J.: Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing. ACM Trans. Graphics 26(3), 1–10 (2007)CrossRef
8.
Liang, C., Lin, T., Wong, B., Liu, C., Chen, H.: Programmable aperture photography: multiplexed light field acquisition. ACM Trans. Graphics 27(3), 1–10 (2008)CrossRef
9.
Taniguchi, Y., Agrawal, A., Veeraraghavan, A., Ramalingam, S., Raskar, R.: Axial-cones: modeling spherical catadioptric cameras for wide-angle light field rendering. ACM Trans. Graphics 29(6), 1–10 (2010)CrossRef
10.
Ng, R., Levoy, M., Bredif, M., Duval, G., Horowitz, M., Hanrahan, P.: Light field photography with a hand-held plenoptic camera. Stanford University Computer Science and Technical Report, vol. 2, no. 11, 2005
11.
Georgiev, T., Lumsdaine, A.: Reducing plenoptic camera artifacts. In: Computer Graphics Forum, vol. 29, no. 6, pp. 1955–1968 (2010)
12.
Lu, H., Li, B., Zhu, J., Li, Y., Li, Y., Xu, X., He, L., Li, X., Li, J., Serikawa, S.: Wound intensity correction and segmentation with convolutional neural networks. Concurr. Comput. Pract. Exp. 27(9), 1–10 (2017)
13.
Wanner, S., Goldluecke, B.: Globally consistent depth labeling of 4D light fields. In Proceedings of CVPR2012, pp. 41–48 (2012)
14.
Tao, M., Hadap, S., Malik, J., Ramamoorthi, R.: Depth from combining defocus and correspondence using light-field cameras. In Proceedings of IEEE ICCV2013, pp. 673–680 (2013)
15.
Tao, M., Wang, T., Malik, J., Ramamoorthi, R.: Depth estimation for glossy surfaces with light-field cameras. In: Workshop on Light Fields for Computer Vision, ECCV (2014)
16.
Jeon, H., Park, J., Choe, G., Park, J., Bok, Y., Tai, Y., Kweon, I.: Accurate depth map estimation from a lenslet light field camera. In: Proceedings of CVPR2015, pp. 1547–1555 (2015)
17.
Wang, W., Efros, A., Ramamoorthi, R.: Occlusion-aware depth estimation using light-field cameras. In Proceedings of ICCV2015, pp. 3487–3495 (2015)
18.
Williem, W., Park, I.: Robust light field depth estimation for noisy scene with occlusion. In: Proceedings Of CVPR2016, pp. 4396–4404 (2010)
19.
Kalantari, N., Wang, T., Ramamoorthi, R.: Learning-based view synthesis for light field cameras. In Proceedings of SIGGRAPH Asia (2016)
20.
Wang, T., Srikanth, M., Ramamoorthi, R.: Depth from semi-calibrated stereo and defocus. In: Proceedings of CVPR (2016)
21.
Wang, T., Chandraker, M., Efros, A., Ramamoorthi, R.: SVBRDF-invariant shape and reflectance estimation from light-field cameras. In Proceedings of CVPR (2016)
22.
Diebel, J., Thrun, S.: An application of Markov radom fields to range sensing. Adv. Neural. Inf. Process. Syst. 18, 291 (2005)
23.
Huhle, B., Fleck, S., Schilling, A.: Integrating 3D time-of-flight camera data and high resolution images for 3DTV applications. In: Proceedings of 3DTV, pp. 1–4 (2007)
24.
Garro, V., Zanuttigh, P., Cortelazzo, G.: A new super resolution technique for range data. In Proceedings of Associazione Gruppo Telecomunicazionie Tecnologie dell Informazione (2009)
25.
Yang, Q., Tan, K., Culbertson, B., Apostolopoulos, J.: Fusion of active and passive sensors for fast 3D capture. In Proceedings of IEEE International Workshop on Multimedia Signal Processing, pp. 69–74 (2010)
26.
Zhu, J., Wang, L., Gao, J., Yang, R.: Spatial-temporal fusion for high accuracy depth maps using dynamic MRFs. IEEE Trans Pattern Anal. Mach. Intell. 32(5), 899–909 (2010)CrossRef
27.
He, K., Sun, J., Tang, X.: Guided image filtering. In: Proceedings of ECCV, pp. 1–14 (2010)
28.
Lu, J., Min, D., Pahwa, R., Do, M.: A revisit to MRF-based depth map super-resolution and enhancement. In: Proceedings of IEEE ICASSP, pp. 985–988 (2011)
29.
Park, J., Kim, H., Tai, Y., Brown, M., Kweon, I.: High quality depth map upsampling for 3D-TOF cameras. In: Proceedings of ICCV, pp. 1623–1630 (2011)
30.
Aodha, O., Campbell, N., Nair, A., Brostow, G.: Patch based synthesis for single depth image super-resolution. In: Proceedings of ECCV, pp. 71–84 (2012)
31.
Min, D., Lu, J., Do, M.: Depth video enhancement based on joint global mode filtering. IEEE Trans. Image Process. 21(3), 1176–1190 (2012)CrossRefMathSciNetMATH
32.
Lu, J., Shi, K., Min, D., Lin, L., Do, M.: Cross-based local multipoint filtering. In Proceedings of CVPR, pp. 430–437 (2012)
33.
Ferstl, D., Reinbacher, C., Ranftl, R., Ruther, M., Bischof, H.: Image guided depth upsampling using anisotropic total generalized variation. In: Proceedings of ICCV, pp. 993–1000 (2013)
34.
Liu, M., Tuzel, O., Taguchi, Y.: Joint geodesic upsampling of depth images. In Proceedings of CVPR, pp. 169–176 (2013)
35.
Serikawa, S., Lu, H.: Underwater image dehazing using joint trilateral filter. Comput. Electr. Eng. 40(1), 41–50 (2014)CrossRef
36.
Lu, H., Li, Y., Zhang, L., Serikawa, S.: Contrast enhancement for images in turbid water. J. Opt. Soc. Am. 32(5), 886–893 (2015)CrossRef
37.
Liu, F., Shen, C., Lin, G., Reid, I.: Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2024–2038 (2016)CrossRef
38.
39.
Lu, H., Zhang, Y., Li, Y., Zhou, Q., Tadoh, R., Uemura, T., Kim, H., Serikawa, S.: Depth map reconstruction for underwater Kinect camera using inpainting and local image mode filtering. IEEE Access 5(1), 7115–7122 (2017)CrossRef
Metadata
Title
Underwater Light Field Depth Map Restoration Using Deep Convolutional Neural Fields
Authors
Huimin Lu
Yujie Li
Hyoungseop Kim
Seiichi Serikawa
Copyright Year
2018
Book
Artificial Intelligence and Robotics

Print ISBN: 978-3-319-69876-2

Electronic ISBN: 978-3-319-69877-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-69877-9_33

Premium Partner

    Image Credits