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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ancuti, C., Ancuti, C.O., Bovik, A., Vleeschouwer, C.D.: Night time dehazing by fusion. In: IEEE ICIP (2016)
Ancuti, C., Ancuti, C.O., Vleeschouwer, C.D.: D-HAZY: A dataset to evaluate quantitatively dehazing algorithms. In: IEEE ICIP (2016)
Ancuti, C.O., Ancuti, C., Hermans, C., Bekaert, P.: A fast semi-inverse approach to detect and remove the haze from a single image. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010. LNCS, vol. 6493, pp. 501–514. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19309-5_39
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)
Ancuti, C., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Trans. Image Process. 22(8), 3271–3282 (2013)
Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: IEEE International Conference on Computer Vision and Pattern Recognition (2016)
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)
Chavez, P.: An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ. 24, 459–479 (1988)
Chen, Z., Jiang, T., Tian, Y.: Quality assessment for comparing image enhancement algorithms. In: IEEE Conference on Computer Vision and Pattern Recognition (2014)
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)
Cozman, F., Krotkov, E.: Depth from scattering. In: IEEE Conference on Computer Vision and Pattern Recognition (1997)
Emberton, S., Chittka, L., Cavallaro, A.: Hierarchical rank-based veiling light estimation for underwater dehazing. In: Proceedings of British Machine Vision Conference (BMVC) (2015)
Fattal, R.: Single image dehazing. SIGGRAPH 27, 72 (2008)
Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. 34, 13 (2014)
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)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: IEEE CVPR (2009)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2341–2353 (2011)
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 6, 1397–1409 (2013)
El Khoury, J., Thomas, J.-B., Mansouri, A.: A color image database for haze model and dehazing methods evaluation. In: Mansouri, A., Nouboud, F., Chalifour, A., Mammass, D., Meunier, J., ElMoataz, A. (eds.) ICISP 2016. LNCS, vol. 9680, pp. 109–117. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33618-3_12
Kopf, J., et al.: Deep photo: model-based photograph enhancement and viewing. Siggraph ASIA ACM Trans. Graph. (2008)
Koschmieder, H.: Theorie der horizontalen sichtweite. In: Beitrage zur Physik der freien Atmosphare (1924)
Kratz, L., Nishino, K.: Factorizing scene albedo and depth from a single foggy image. In: ICCV (2009)
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)
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)
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)
Narasimhan, S., Nayar, S.: Vision and the atmosphere. Int. J. Comput. Vis. 48, 233–254 (2002)
Narasimhan, S., Nayar, S.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. (2003)
Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.-H.: Single image dehazing via multi-scale convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 154–169. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_10
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)
Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Polarization-based vision through haze. Appl. Opt. 42, 511–525 (2003)
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)
Tan, R.T.: Visibility in bad weather from a single image. In IEEE Conference on Computer Vision and Pattern Recognition (2008)
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)
Tarel, J.P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: IEEE ICCV (2009)
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)
Wang, Z., Bovik, A.C.: Modern Image Quality Assessment. Morgan and Claypool Publishers, New York (2006)
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)
Westland, S., Ripamonti, C., Cheung, V.: Computational Colour Science Using MATLAB, 2nd edn. Wiley, New York (2005)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-01449-0_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01448-3
Online ISBN: 978-3-030-01449-0
eBook Packages: Computer ScienceComputer Science (R0)