Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Improving a Switched Vector Field Model for Pedestrian Motion Analysis Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

I-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Indoor Images

verfasst von : Cosmin Ancuti, Codruta O. Ancuti, Radu Timofte, Christophe De Vleeschouwer

Erschienen in: Advanced Concepts for Intelligent Vision Systems

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Image dehazing has become an important computational imaging topic in the recent years. However, due to the lack of ground truth images, the comparison of dehazing methods is not straightforward, nor objective. To overcome this issue we introduce I-HAZE, a new dataset that contains 35 image pairs of hazy and corresponding haze-free (ground-truth) indoor images. Different from most of the existing dehazing databases, hazy images have been generated using real haze produced by a professional haze machine. To ease color calibration and improve the assessment of dehazing algorithms, each scene includes a MacBeth color checker. Moreover, since the images are captured in a controlled environment, both haze-free and hazy images are captured under the same illumination conditions. This represents an important advantage of the I-HAZE dataset that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM.

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Vorheriges Kapitel Relocated Colour Contrast Occurrence Matrix and Adapted Similarity Measure for Colour Texture Retrieval
Fußnoten
Literatur
1.
Ancuti, C., Ancuti, C.O., Bovik, A., Vleeschouwer, C.D.: Night time dehazing by fusion. In: IEEE ICIP (2016)
2.
Ancuti, C., Ancuti, C.O., Vleeschouwer, C.D.: D-HAZY: A dataset to evaluate quantitatively dehazing algorithms. In: IEEE ICIP (2016)
3.
4.
Ancuti, C.O., Ancuti, C., Timofte, R., Vleeschouwer, C.D.: O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images. In: IEEE CVPR, NTIRE Workshop (2018)
5.
Ancuti, C., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Trans. Image Process. 22(8), 3271–3282 (2013)CrossRef
6.
Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: IEEE International Conference on Computer Vision and Pattern Recognition (2016)
7.
Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: Dehazenet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25, 5187–5198 (2016)MathSciNetCrossRefMATH
8.
Chavez, P.: An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ. 24, 459–479 (1988)CrossRef
9.
Chen, Z., Jiang, T., Tian, Y.: Quality assessment for comparing image enhancement algorithms. In: IEEE Conference on Computer Vision and Pattern Recognition (2014)
10.
Choi, L.K., You, J., Bovik, A.C.: Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24, 3888–3901 (2015)MathSciNetCrossRefMATH
11.
Cozman, F., Krotkov, E.: Depth from scattering. In: IEEE Conference on Computer Vision and Pattern Recognition (1997)
12.
Emberton, S., Chittka, L., Cavallaro, A.: Hierarchical rank-based veiling light estimation for underwater dehazing. In: Proceedings of British Machine Vision Conference (BMVC) (2015)
13.
Fattal, R.: Single image dehazing. SIGGRAPH 27, 72 (2008)
14.
Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. 34, 13 (2014)CrossRef
15.
Hautiere, N., Tarel, J.P., Aubert, D., Dumont, E.: Blind contrast enhancement assessment by gradient ratioing at visible edges. J. Image Anal. Stereol. 27, 87–95 (2008)MathSciNetCrossRefMATH
16.
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: IEEE CVPR (2009)
17.
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2341–2353 (2011)CrossRef
18.
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 6, 1397–1409 (2013)CrossRef
19.
20.
Kopf, J., et al.: Deep photo: model-based photograph enhancement and viewing. Siggraph ASIA ACM Trans. Graph. (2008)
21.
Koschmieder, H.: Theorie der horizontalen sichtweite. In: Beitrage zur Physik der freien Atmosphare (1924)
22.
Kratz, L., Nishino, K.: Factorizing scene albedo and depth from a single foggy image. In: ICCV (2009)
23.
Li, Y., Tan, R.T., Brown, M.S.: Nighttime haze removal with glow and multiple light colors. In: IEEE International Conference on Computer Vision (2015)
24.
Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: IEEE International Conference on Computer Vision (2013)
25.
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21, 4695–4708 (2012)MathSciNetCrossRefMATH
26.
Narasimhan, S., Nayar, S.: Vision and the atmosphere. Int. J. Comput. Vis. 48, 233–254 (2002)CrossRefMATH
27.
Narasimhan, S., Nayar, S.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. (2003)
28.
29.
Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image Process. 21, 3339–3352 (2012)MathSciNetCrossRefMATH
30.
Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Polarization-based vision through haze. Appl. Opt. 42, 511–525 (2003)CrossRef
31.
Sharma, G., Wu, W., Dalal, E.: The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Res. Appl. 30, 21–30 (2005)CrossRef
32.
Tan, R.T.: Visibility in bad weather from a single image. In IEEE Conference on Computer Vision and Pattern Recognition (2008)
33.
Tang, K., Yang, J., Wang, J.: Investigating haze-relevant features in a learning framework for image dehazing. In IEEE Conference on Computer Vision and Pattern Recognition (2014)
34.
Tarel, J.P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: IEEE ICCV (2009)
35.
Tarel, J.P., Hautire, N., Caraffa, L., Cord, A., Halmaoui, H., Gruyer, D.: Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell. Transp. Syst. Mag. 4, 6–20 (2012)CrossRef
36.
Wang, Z., Bovik, A.C.: Modern Image Quality Assessment. Morgan and Claypool Publishers, New York (2006)
37.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)CrossRef
38.
Westland, S., Ripamonti, C., Cheung, V.: Computational Colour Science Using MATLAB, 2nd edn. Wiley, New York (2005)
39.
Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24, 3522–3533 (2015)MathSciNetCrossRefMATH
Metadaten
Titel
I-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Indoor Images
verfasst von
Cosmin Ancuti
Codruta O. Ancuti
Radu Timofte
Christophe De Vleeschouwer
Copyright-Jahr
2018
Buch
Advanced Concepts for Intelligent Vision Systems

Print ISBN: 978-3-030-01448-3

Electronic ISBN: 978-3-030-01449-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01449-0_52

Premium Partner

    Bildnachweise