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

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11182))

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.

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Notes

  1. 1.

    http://vision.middlebury.edu/stereo/data/scenes2014/.

  2. 2.

    http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html.

  3. 3.

    http://www.vision.ee.ethz.ch/ntire18/i-haze/.

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Author information

Authors and Affiliations

  1. MEO, Universitatea Politehnica Timisoara, Timişoara, Romania

    Cosmin Ancuti & Codruta O. Ancuti

  2. ICTEAM, Universite Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium

    Christophe De Vleeschouwer

  3. ETH Zurich, Zürich, Switzerland

    Radu Timofte

  4. Merantix GmbH, Berlin, Germany

    Radu Timofte

Authors
  1. Cosmin Ancuti
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  2. Codruta O. Ancuti
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  3. Radu Timofte
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  4. Christophe De Vleeschouwer
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Corresponding author

Correspondence to Cosmin Ancuti .

Editor information

Editors and Affiliations

  1. DGA, Bagneux, France

    Jacques Blanc-Talon

  2. Laboratoire XLIM, Futuroscope Chasseneuil Cedex, France

    David Helbert

  3. Ghent University, Ghent, Belgium

    Wilfried Philips

  4. CSIRO-ICT Centre, Canberra, ACT, Australia

    Dan Popescu

  5. University of Antwerp, Wilrijk, Belgium

    Paul Scheunders

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Ancuti, C., Ancuti, C.O., Timofte, R., De Vleeschouwer, C. (2018). I-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Indoor Images. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_52

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01448-3

  • Online ISBN: 978-3-030-01449-0

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