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RGBN Multispectral Images: A Novel Color Restoration Approach

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Book cover Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017 (PAAMS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 619))

Abstract

This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired—referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a naïve color correction technique based on mean square error minimization are provided.

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Notes

  1. 1.

    Demosaicking refers to obtaining the {R,G,B,NIR} components from a given pixel, where all the information is attached together is a single square array {B,G} in top and {IR,R} from the bottom, see an illustration of this pixel composition in Fig. 2.

  2. 2.

    https://www.e-consystems.com/.

  3. 3.

    http://www.cvc.uab.es/~asappa/Color_Correction_Dataset.rar.

  4. 4.

    http://iatool.net/.

References

  1. Salamati, N., Fredembach, C., Süsstrunk, S.: Material classification using color and NIR images. In: Color and Imaging Conference, Society for Imaging Science and Technology, vol. 2009, pp. 216–222 (2009)

    Google Scholar 

  2. Ricaurte, P., Chilán, C., Aguilera-Carrasco, C.A., Vintimilla, B.X., Sappa, A.D.: Feature point descriptors: infrared and visible spectra. Sensors 14, 3690–3701 (2014)

    Article  Google Scholar 

  3. Barrera, F., Lumbreras, F., Sappa, A.D.: Evaluation of similarity functions in multimodal stereo. In: International Conference Image Analysis and Recognition, pp. 320–329, Springer (2012)

    Google Scholar 

  4. Mouats, T., Aouf, N., Sappa, A.D., Aguilera, C., Toledo, R.: Multispectral stereo odometry. IEEE Trans. Intell. Trans. Syst. 16, 1210–1224 (2015)

    Article  Google Scholar 

  5. Mountrakis, G., Im, J., Ogole, C.: Support vector machines in remote sensing: a review. ISPRS J. Photogrammetry Remote Sens. 66, 247–259 (2011)

    Article  Google Scholar 

  6. Adam, E., Mutanga, O., Rugege, D.: Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol. Manag. 18, 281–296 (2010)

    Article  Google Scholar 

  7. Barnard, K., Cardei, V., Funt, B.: A comparison of computational color constancy algorithms. I: methodology and experiments with synthesized data. IEEE Trans. Image Process. 11, 972–984 (2002)

    Article  Google Scholar 

  8. Chen, Z., Zhu, N., Pacheco, S., Wang, X., Liang, R.: Single camera imaging system for color and near-infrared fluorescence image guided surgery. Biomed. Opt. Express 5, 2791–2797 (2014)

    Article  Google Scholar 

  9. Chen, Z., Wang, X., Liang, R.: RGB-NIR multispectral camera. Opt. Express 22, 4985–4994 (2014)

    Article  Google Scholar 

  10. Martinello, M., Wajs, A., Quan, S., Lee, H., Lim, C., Woo, T., Lee, W., Kim, S.S., Lee, D.: Dual aperture photography: image and depth from a mobile camera. In: 2015 IEEE International Conference on Computational Photography (ICCP), pp. 1–10 (2015)

    Google Scholar 

  11. Park, C.H., Oh, H.M., Kang, M.G.: Color restoration for infrared cutoff filter removed RGBN multispectral filter array image sensor. In: VISAPP, (1), pp. 30–37 (2015)

    Google Scholar 

  12. Park, C., Kang, M.G.: Color restoration of rgbn multispectral filter array sensor images based on spectral decomposition. Sensors 16, 719 (2016)

    Article  Google Scholar 

  13. Teranaka, H., Monno, Y., Tanaka, M., Ok, M.: Single-sensor RGB and NIR image acquisition: toward optimal performance by taking account of CFA pattern, demosaicking, and color correction. Electronic Imaging 2016, pp. 1–6 (2016)

    Google Scholar 

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Acknowledgments

This work has been partially supported by the Spanish Government under Project TIN2014-56919-C3-2-R and by the ESPOL projects: “Pattern recognition: case study on agriculture and aquaculture” (M1-DI-2015) and “Integrated system for emergency management using sensor networks and reactive signaling” (G4-DI-2014). Cristhian Aguilera has been supported by Universitat Autònoma de Barcelona. Xavier Soria would like to thank to Ecuador government under a scholarship contract 2015-AR3R7694.

Author information

Authors and Affiliations

  1. Computer Vision Center, Edifici O, Campus UAB, 08193, Bellaterra, Barcelona, Spain

    Cristhian Aguilera, Xavier Soria, Angel D. Sappa & Ricardo Toledo

  2. Facultad de Ingeniería en Electricidad y Computación, CIDIS, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo, 09-01-5863, Guayaquil, Ecuador

    Angel D. Sappa

Authors
  1. Cristhian Aguilera
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  2. Xavier Soria
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  3. Angel D. Sappa
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  4. Ricardo Toledo
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Corresponding author

Correspondence to Angel D. Sappa .

Editor information

Editors and Affiliations

  1. Departamento de Informática y Automática, Facultad de Ciencias, Universidad de Salamanca, Salamanca, Spain

    Fernando De la Prieta

  2. GECAD Instituto Superior de Engenharia do Porto, Porto, Portugal

    Zita Vale

  3. Department of Informática, Universidade de Lisboa Faculdade de Ciências, Lisboa, Portugal

    Luis Antunes

  4. Facultad de Ciencias, Departamento de Informática y Automática, Salamanca, Spain

    Tiago Pinto

  5. School of Computer Science, Dartmouth College, Hanover, New Hampshire, USA

    Andrew T. Campbell

  6. Departamento de Sistemas Informáticos y Computación, Universidad Politecnica de Valencia, Valencia, Spain

    Vicente Julián

  7. University of Aveiro, Institute of Electronics and Telematics Engineering, Aveiro, Portugal

    Antonio J.R. Neves

  8. Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Salamanca, Salamanca, Spain

    María N. Moreno

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Aguilera, C., Soria, X., Sappa, A.D., Toledo, R. (2018). RGBN Multispectral Images: A Novel Color Restoration Approach. In: De la Prieta, F., et al. Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. PAAMS 2017. Advances in Intelligent Systems and Computing, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-319-61578-3_15

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  • DOI: https://doi.org/10.1007/978-3-319-61578-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61577-6

  • Online ISBN: 978-3-319-61578-3

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