Skip to main content

2019 | OriginalPaper | Buchkapitel

Physically Plausible Dehazing for Non-physical Dehazing Algorithms

verfasst von : Javier Vazquez-Corral, Graham D. Finlayson, Marcelo Bertalmío

Erschienen in: Computational Color Imaging

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Images affected by haze usually present faded colours and loss of contrast, hindering the precision of methods devised for clear images. For this reason, image dehazing is a crucial pre-processing step for applications such as self-driving vehicles or tracking. Some of the most successful dehazing methods in the literature do not follow any physical model and are just based on either image enhancement or image fusion. In this paper, we present a procedure to allow these methods to accomplish the Koschmieder physical model, i.e., to force them to have a unique transmission for all the channels, instead of the per-channel transmission they obtain. Our method is based on coupling the results obtained for each of the three colour channels. It improves the results of the original methods both quantitatively using image metrics, and subjectively via a psychophysical test. It especially helps in terms of avoiding over-saturation and reducing colour artefacts, which are the most common complications faced by image dehazing methods.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

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!

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!

Literatur
2.
Zurück zum Zitat Ancuti, C., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Trans. Image Process. 22(8), 3271–3282 (2013)CrossRef Ancuti, C., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Trans. Image Process. 22(8), 3271–3282 (2013)CrossRef
3.
Zurück zum Zitat Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
4.
Zurück zum Zitat Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: DehazeNet: An End-to-End System for Single Image Haze Removal, January 2016. arXiv:1601.07661 Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: DehazeNet: An End-to-End System for Single Image Haze Removal, January 2016. arXiv:​1601.​07661
6.
Zurück zum Zitat Choi, L.K., You, J., Bovik, A.C.: Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24(11), 3888–3901 (2015)MathSciNetCrossRef Choi, L.K., You, J., Bovik, A.C.: Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24(11), 3888–3901 (2015)MathSciNetCrossRef
7.
Zurück zum Zitat Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. 34, 1 (2014)CrossRef Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. 34, 1 (2014)CrossRef
9.
Zurück zum Zitat Fu, X., Zeng, D., Huang, Y., Zhang, X.P., Ding, X.: A weighted variational model for simultaneous reflectance and illumination estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2782–2790, June 2016. https://doi.org/10.1109/CVPR.2016.304 Fu, X., Zeng, D., Huang, Y., Zhang, X.P., Ding, X.: A weighted variational model for simultaneous reflectance and illumination estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2782–2790, June 2016. https://​doi.​org/​10.​1109/​CVPR.​2016.​304
10.
Zurück zum Zitat Galdran, A., Alvarez-Gila, A., Bria, A., Vazquez-Corral, J., Bertalmío, M.: On the duality between retinex and image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) Galdran, A., Alvarez-Gila, A., Bria, A., Vazquez-Corral, J., Bertalmío, M.: On the duality between retinex and image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
11.
Zurück zum Zitat Galdran, A., Vazquez-Corral, J., Pardo, D., Bertalmío, M.: Enhanced variational image dehazing. SIAM J. Imaging Sci. 8(3), 1519–1546 (2015)MathSciNetCrossRef Galdran, A., Vazquez-Corral, J., Pardo, D., Bertalmío, M.: Enhanced variational image dehazing. SIAM J. Imaging Sci. 8(3), 1519–1546 (2015)MathSciNetCrossRef
13.
Zurück zum Zitat He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)CrossRef He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)CrossRef
14.
Zurück zum Zitat Khoury, J.E., Moan, S.L., Thomas, J., Mansouri, A.: Color and sharpness assessment of single image dehazing. Multimedia Tools Appl. 77(12), 15409–15430 (2018)CrossRef Khoury, J.E., Moan, S.L., Thomas, J., Mansouri, A.: Color and sharpness assessment of single image dehazing. Multimedia Tools Appl. 77(12), 15409–15430 (2018)CrossRef
15.
Zurück zum Zitat Koschmieder, H.: Theorie der horizontalen Sichtweite: Kontrast und Sichtweite. Keim & Nemnich (1925) Koschmieder, H.: Theorie der horizontalen Sichtweite: Kontrast und Sichtweite. Keim & Nemnich (1925)
17.
Zurück zum Zitat Matlin, E., Milanfar, P.: Removal of haze and noise from a single image. In: Proceedings of SPIE 8296. Computational Imaging X, vol. 8296, pp. 82960T–82960T-12 (2012) Matlin, E., Milanfar, P.: Removal of haze and noise from a single image. In: Proceedings of SPIE 8296. Computational Imaging X, vol. 8296, pp. 82960T–82960T-12 (2012)
18.
Zurück zum Zitat Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 617–624, December 2013 Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 617–624, December 2013
20.
Zurück zum Zitat Mittal, A., Soundararajan, R., Bovik, A.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20, 209–212 (2013)CrossRef Mittal, A., Soundararajan, R., Bovik, A.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20, 209–212 (2013)CrossRef
21.
23.
Zurück zum Zitat Tan, R.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8, June 2008 Tan, R.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8, June 2008
24.
Zurück zum Zitat Tarel, J.P., Hautiere, N., Caraffa, L., Cord, A., Halmaoui, H., Gruyer, D.: Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012)CrossRef Tarel, J.P., Hautiere, N., Caraffa, L., Cord, A., Halmaoui, H., Gruyer, D.: Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012)CrossRef
27.
Zurück zum Zitat Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Metadaten
Titel
Physically Plausible Dehazing for Non-physical Dehazing Algorithms
verfasst von
Javier Vazquez-Corral
Graham D. Finlayson
Marcelo Bertalmío
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-13940-7_18