Abstract

Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast. To improve their visibility, a process called image dehazing can be applied. Some of the most successful image dehazing algorithms are based on image processing methods but do not follow any physical image formation model, which limits their performance. In this paper, we propose a post-processing technique to alleviate this handicap by enforcing the original method to be consistent with a popular physical model for image formation under haze. Our results improve upon those of the original methods qualitatively and according to several metrics, and they have also been validated via psychophysical experiments. These results are particularly striking in terms of avoiding over-saturation and reducing color artifacts, which are the most common shortcomings faced by image dehazing methods.

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References

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  29. L. K. Choi, J. You, and A. C. Bovik, “Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging,” IEEE Transactions on Image Process. 24(11), 3888–3901 (2015).
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    [Crossref]

2017 (1)

2016 (2)

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Fusion-based variational image dehazing,” IEEE Signal Process. Lett. 24, 1 (2016).
[Crossref]

Y. Wang, H. Wang, C. Yin, and M. Dai, “Biologically inspired image enhancement based on Retinex,” Neurocomputing 177, 373–384 (2016).
[Crossref]

2015 (8)

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Enhanced Variational Image Dehazing,” SIAM J. on Imaging Sci. 8(3), 1519–1546 (2015).
[Crossref]

X.-S. Zhang, S.-B. Gao, C.-Y. Li, and Y.-J. Li, “A Retina Inspired Model for Enhancing Visibility of Hazy Images,” Front. Comput. Neurosci. 9, 151 (2015).
[Crossref]

L. K. Choi, J. You, and A. C. Bovik, “Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging,” IEEE Transactions on Image Process. 24(11), 3888–3901 (2015).
[Crossref]

J.-B. Wang, N. He, L.-L. Zhang, and K. Lu, “Single image dehazing with a physical model and dark channel prior,” Neurocomputing 149, 718–728 (2015).
[Crossref]

Z. Li and J. Zheng, “Edge-Preserving Decomposition-Based Single Image Haze Removal,” IEEE Transactions on Image Process. 24(12), 5432–5441 (2015).
[Crossref]

Y.-H. Lai, Y.-L. Chen, C.-J. Chiou, and C.-T. Hsu, “Single-Image Dehazing via Optimal Transmission Map Under Scene Priors,” IEEE Transactions on Circuits Syst. for Video Technol. 25(1), 1–14 (2015).

Q. Zhu, J. Mai, and L. Shao, “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior,” IEEE Transactions on Image Process. 24(11), 3522–3533 (2015).
[Crossref]

G. D. Finlayson, M. M. Darrodi, and M. Mackiewicz, “The alternating least squares technique for nonuniform intensity color correction,” Color Res. Appl. 40(3), 232–242 (2015).
[Crossref]

2014 (2)

A. B. Petro, C. Sbert, and J.-M. Morel, “Multiscale Retinex,” Image Processing On Line 4, 71–88 (2014).
[Crossref]

Y. Gao, H.-M. Hu, S. Wang, and B. Li, “A fast image dehazing algorithm based on negative correction,” Signal Process. 103, 380–398 (2014).
[Crossref]

2013 (4)

W. Sun, “A new single-image fog removal algorithm based on physical model,” Optik 124(21), 4770–4775 (2013).
[Crossref]

C. Ancuti and C. Ancuti, “Single Image Dehazing by Multi-Scale Fusion,” IEEE Transactions on Image Process. 22(8), 3271–3282 (2013).
[Crossref]

A. Mittal, R. Soundararajan, and A. Bovik, “Making a completely blind image quality analyzer,” Signal Process. Lett. IEEE 20(3), 209–212 (2013).
[Crossref]

I. Lissner, J. Preiss, P. Urban, M. S. Lichtenauer, and P. Zolliker, “Image-difference prediction: From grayscale to color,” IEEE Transactions on Image Process. 22(2), 435–446 (2013).
[Crossref]

2012 (3)

A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on Image Process. 21(12), 4695–4708 (2012).
[Crossref]

E. Matlin and P. Milanfar, “Removal of haze and noise from a single image,” Proc. SPIE 8296, 82960T (2012).
[Crossref]

K. Nishino, L. Kratz, and S. Lombardi, “Bayesian Defogging,” Int. J. Comput. Vis. 98(3), 263–278 (2012).
[Crossref]

2011 (1)

K. He, J. Sun, and X. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Transactions on Pattern Analysis Mach. Intell. 33(12), 2341–2353 (2011).
[Crossref]

2007 (1)

J. Oakley and H. Bu, “Correction of Simple Contrast Loss in Color Images,” IEEE Transactions on Image Process. 16(2), 511–522 (2007).
[Crossref]

2006 (1)

H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Transactions on Image Process. 15(2), 430–444 (2006).
[Crossref]

1997 (2)

D. H. Brainard, “The Psychophysics Toolbox,” Spat Vis. 10(4), 433–436 (1997).
[Crossref]

D. G. Pelli, “The VideoToolbox software for visual psychophysics: transforming numbers into movies,” Spat Vis. 10(4), 437–442 (1997).
[Crossref]

Abidi, M. A.

Alvarez-Gila, A.

A. Galdran, A. Alvarez-Gila, A. Bria, J. Vazquez-Corral, and M. Bertalmío, “On the duality between retinex and image dehazing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2018).

Ancuti, C.

C. Ancuti and C. Ancuti, “Single Image Dehazing by Multi-Scale Fusion,” IEEE Transactions on Image Process. 22(8), 3271–3282 (2013).
[Crossref]

C. Ancuti and C. Ancuti, “Single Image Dehazing by Multi-Scale Fusion,” IEEE Transactions on Image Process. 22(8), 3271–3282 (2013).
[Crossref]

C. O. Ancuti, C. Ancuti, C. Hermans, and P. Bekaert, “A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image, in Asian Conference on Computer Vision, ACCV-2010, (2010) 6493, pp. 501–514.

C. Ancuti, C. O. Ancuti, and C. D. Vleeschouwer, “D-hazy: A dataset to evaluate quantitatively dehazing algorithms,” in Proceedings of the IEEE International Conference on Image Processing, (IEEE, 2016), CIP’16.

Ancuti, C. O.

C. Ancuti, C. O. Ancuti, and C. D. Vleeschouwer, “D-hazy: A dataset to evaluate quantitatively dehazing algorithms,” in Proceedings of the IEEE International Conference on Image Processing, (IEEE, 2016), CIP’16.

C. O. Ancuti, C. Ancuti, C. Hermans, and P. Bekaert, “A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image, in Asian Conference on Computer Vision, ACCV-2010, (2010) 6493, pp. 501–514.

Bekaert, P.

C. O. Ancuti, C. Ancuti, C. Hermans, and P. Bekaert, “A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image, in Asian Conference on Computer Vision, ACCV-2010, (2010) 6493, pp. 501–514.

Bertalmío, M.

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Fusion-based variational image dehazing,” IEEE Signal Process. Lett. 24, 1 (2016).
[Crossref]

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Enhanced Variational Image Dehazing,” SIAM J. on Imaging Sci. 8(3), 1519–1546 (2015).
[Crossref]

A. Galdran, A. Alvarez-Gila, A. Bria, J. Vazquez-Corral, and M. Bertalmío, “On the duality between retinex and image dehazing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2018).

J. Vazquez-Corral, G. D. Finlayson, and M. Bertalmío, “Physically plausible dehazing for non-physical dehazing algorithms,” in Computational Color Imaging, S. Tominaga, R. Schettini, A. Trémeau, and T. Horiuchi, eds. (Springer International Publishing, 2019), pp. 233–244.

J. Vazquez-Corral, A. Galdran, P. Cyriac, and M. Bertalmío, “A fast image dehazing method that does not introduce color artifacts,” Journal of Real-Time Image Processing, pp. doi: 10.1007/s11554–018–0816–6 posted 29 August 2018, in press.

Bovik, A.

A. Mittal, R. Soundararajan, and A. Bovik, “Making a completely blind image quality analyzer,” Signal Process. Lett. IEEE 20(3), 209–212 (2013).
[Crossref]

Bovik, A. C.

L. K. Choi, J. You, and A. C. Bovik, “Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging,” IEEE Transactions on Image Process. 24(11), 3888–3901 (2015).
[Crossref]

A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on Image Process. 21(12), 4695–4708 (2012).
[Crossref]

H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Transactions on Image Process. 15(2), 430–444 (2006).
[Crossref]

Brainard, D. H.

D. H. Brainard, “The Psychophysics Toolbox,” Spat Vis. 10(4), 433–436 (1997).
[Crossref]

Bria, A.

A. Galdran, A. Alvarez-Gila, A. Bria, J. Vazquez-Corral, and M. Bertalmío, “On the duality between retinex and image dehazing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2018).

Brown, M. S.

Y. Li, F. Guo, R. T. Tan, and M. S. Brown, “A contrast enhancement framework with jpeg artifacts suppression,” in Computer Vision - ECCV 2014 - 13th European Conference, (2014), pp. 174–188.

Bu, H.

J. Oakley and H. Bu, “Correction of Simple Contrast Loss in Color Images,” IEEE Transactions on Image Process. 16(2), 511–522 (2007).
[Crossref]

Cai, B.

B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “DehazeNet: An End-to-End System for Single Image Haze Removal,” arXiv:1601.07661 (2016).

Chen, C.

C. Chen, M. N. Do, and J. Wang, “Robust image and video dehazing with visual artifact suppression via gradient residual minimization, in Computer Vision - ECCV 2016 - 14th European Conference, (2016), pp. 576–591.

Chen, Y.-L.

Y.-H. Lai, Y.-L. Chen, C.-J. Chiou, and C.-T. Hsu, “Single-Image Dehazing via Optimal Transmission Map Under Scene Priors,” IEEE Transactions on Circuits Syst. for Video Technol. 25(1), 1–14 (2015).

Chiou, C.-J.

Y.-H. Lai, Y.-L. Chen, C.-J. Chiou, and C.-T. Hsu, “Single-Image Dehazing via Optimal Transmission Map Under Scene Priors,” IEEE Transactions on Circuits Syst. for Video Technol. 25(1), 1–14 (2015).

Cho, W.

Choi, L. K.

L. K. Choi, J. You, and A. C. Bovik, “Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging,” IEEE Transactions on Image Process. 24(11), 3888–3901 (2015).
[Crossref]

Cyriac, P.

J. Vazquez-Corral, A. Galdran, P. Cyriac, and M. Bertalmío, “A fast image dehazing method that does not introduce color artifacts,” Journal of Real-Time Image Processing, pp. doi: 10.1007/s11554–018–0816–6 posted 29 August 2018, in press.

Dai, M.

Y. Wang, H. Wang, C. Yin, and M. Dai, “Biologically inspired image enhancement based on Retinex,” Neurocomputing 177, 373–384 (2016).
[Crossref]

Darrodi, M. M.

G. D. Finlayson, M. M. Darrodi, and M. Mackiewicz, “The alternating least squares technique for nonuniform intensity color correction,” Color Res. Appl. 40(3), 232–242 (2015).
[Crossref]

De Dravo, V.

V. De Dravo and J. Hardeberg, “Stress for dehazing, in Colour and Visual Computing Symposium (CVCS), 2015, (2015), pp. 1–6.

Ding, X.

X. Fu, D. Zeng, Y. Huang, X. P. Zhang, and X. Ding, “A weighted variational model for simultaneous reflectance and illumination estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 2782–2790.

Do, M. N.

C. Chen, M. N. Do, and J. Wang, “Robust image and video dehazing with visual artifact suppression via gradient residual minimization, in Computer Vision - ECCV 2016 - 14th European Conference, (2016), pp. 576–591.

Duan, J.

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient Image Dehazing with Boundary Constraint and Contextual Regularization,” in Proceedings of IEEE International Conference on Computer Vision (ICCV), (IEEE, 2013), pp. 617–624.

Fattal, R.

R. Fattal, “Single Image Dehazing,” in ACM SIGGRAPH 2008 Papers, (ACM, 2008), SIGGRAPH ’08, pp. 72:1–72:9.

Finlayson, G. D.

G. D. Finlayson, M. M. Darrodi, and M. Mackiewicz, “The alternating least squares technique for nonuniform intensity color correction,” Color Res. Appl. 40(3), 232–242 (2015).
[Crossref]

J. Vazquez-Corral, G. D. Finlayson, and M. Bertalmío, “Physically plausible dehazing for non-physical dehazing algorithms,” in Computational Color Imaging, S. Tominaga, R. Schettini, A. Trémeau, and T. Horiuchi, eds. (Springer International Publishing, 2019), pp. 233–244.

Fu, X.

X. Fu, D. Zeng, Y. Huang, X. P. Zhang, and X. Ding, “A weighted variational model for simultaneous reflectance and illumination estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 2782–2790.

Galdran, A.

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Fusion-based variational image dehazing,” IEEE Signal Process. Lett. 24, 1 (2016).
[Crossref]

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Enhanced Variational Image Dehazing,” SIAM J. on Imaging Sci. 8(3), 1519–1546 (2015).
[Crossref]

A. Galdran, A. Alvarez-Gila, A. Bria, J. Vazquez-Corral, and M. Bertalmío, “On the duality between retinex and image dehazing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2018).

J. Vazquez-Corral, A. Galdran, P. Cyriac, and M. Bertalmío, “A fast image dehazing method that does not introduce color artifacts,” Journal of Real-Time Image Processing, pp. doi: 10.1007/s11554–018–0816–6 posted 29 August 2018, in press.

Gao, S.-B.

X.-S. Zhang, S.-B. Gao, C.-Y. Li, and Y.-J. Li, “A Retina Inspired Model for Enhancing Visibility of Hazy Images,” Front. Comput. Neurosci. 9, 151 (2015).
[Crossref]

Gao, Y.

Y. Gao, H.-M. Hu, S. Wang, and B. Li, “A fast image dehazing algorithm based on negative correction,” Signal Process. 103, 380–398 (2014).
[Crossref]

Guo, F.

Y. Li, F. Guo, R. T. Tan, and M. S. Brown, “A contrast enhancement framework with jpeg artifacts suppression,” in Computer Vision - ECCV 2014 - 13th European Conference, (2014), pp. 174–188.

Hardeberg, J.

V. De Dravo and J. Hardeberg, “Stress for dehazing, in Colour and Visual Computing Symposium (CVCS), 2015, (2015), pp. 1–6.

Hautiere, N.

J.-P. Tarel and N. Hautiere, “Fast visibility restoration from a single color or gray level image,” in Proceedings of IEEE International Conference on Computer Vision, (IEEE, 2009), pp. 2201–2208.

He, K.

K. He, J. Sun, and X. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Transactions on Pattern Analysis Mach. Intell. 33(12), 2341–2353 (2011).
[Crossref]

He, N.

J.-B. Wang, N. He, L.-L. Zhang, and K. Lu, “Single image dehazing with a physical model and dark channel prior,” Neurocomputing 149, 718–728 (2015).
[Crossref]

Hermans, C.

C. O. Ancuti, C. Ancuti, C. Hermans, and P. Bekaert, “A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image, in Asian Conference on Computer Vision, ACCV-2010, (2010) 6493, pp. 501–514.

Hsu, C.-T.

Y.-H. Lai, Y.-L. Chen, C.-J. Chiou, and C.-T. Hsu, “Single-Image Dehazing via Optimal Transmission Map Under Scene Priors,” IEEE Transactions on Circuits Syst. for Video Technol. 25(1), 1–14 (2015).

Hu, H.-M.

Y. Gao, H.-M. Hu, S. Wang, and B. Li, “A fast image dehazing algorithm based on negative correction,” Signal Process. 103, 380–398 (2014).
[Crossref]

Huang, Y.

X. Fu, D. Zeng, Y. Huang, X. P. Zhang, and X. Ding, “A weighted variational model for simultaneous reflectance and illumination estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 2782–2790.

Jang, J.

Jia, K.

B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “DehazeNet: An End-to-End System for Single Image Haze Removal,” arXiv:1601.07661 (2016).

Koschmieder, H.

H. Koschmieder, Theorie der horizontalen Sichtweite: Kontrast und Sichtweite (Keim & Nemnich, 1925).

Kratz, L.

K. Nishino, L. Kratz, and S. Lombardi, “Bayesian Defogging,” Int. J. Comput. Vis. 98(3), 263–278 (2012).
[Crossref]

Lai, Y.-H.

Y.-H. Lai, Y.-L. Chen, C.-J. Chiou, and C.-T. Hsu, “Single-Image Dehazing via Optimal Transmission Map Under Scene Priors,” IEEE Transactions on Circuits Syst. for Video Technol. 25(1), 1–14 (2015).

Li, B.

Y. Gao, H.-M. Hu, S. Wang, and B. Li, “A fast image dehazing algorithm based on negative correction,” Signal Process. 103, 380–398 (2014).
[Crossref]

Li, C.-Y.

X.-S. Zhang, S.-B. Gao, C.-Y. Li, and Y.-J. Li, “A Retina Inspired Model for Enhancing Visibility of Hazy Images,” Front. Comput. Neurosci. 9, 151 (2015).
[Crossref]

Li, Y.

Y. Li, F. Guo, R. T. Tan, and M. S. Brown, “A contrast enhancement framework with jpeg artifacts suppression,” in Computer Vision - ECCV 2014 - 13th European Conference, (2014), pp. 174–188.

Li, Y.-J.

X.-S. Zhang, S.-B. Gao, C.-Y. Li, and Y.-J. Li, “A Retina Inspired Model for Enhancing Visibility of Hazy Images,” Front. Comput. Neurosci. 9, 151 (2015).
[Crossref]

Li, Z.

Z. Li and J. Zheng, “Edge-Preserving Decomposition-Based Single Image Haze Removal,” IEEE Transactions on Image Process. 24(12), 5432–5441 (2015).
[Crossref]

Lichtenauer, M. S.

I. Lissner, J. Preiss, P. Urban, M. S. Lichtenauer, and P. Zolliker, “Image-difference prediction: From grayscale to color,” IEEE Transactions on Image Process. 22(2), 435–446 (2013).
[Crossref]

Lissner, I.

I. Lissner, J. Preiss, P. Urban, M. S. Lichtenauer, and P. Zolliker, “Image-difference prediction: From grayscale to color,” IEEE Transactions on Image Process. 22(2), 435–446 (2013).
[Crossref]

Lombardi, S.

K. Nishino, L. Kratz, and S. Lombardi, “Bayesian Defogging,” Int. J. Comput. Vis. 98(3), 263–278 (2012).
[Crossref]

Lu, K.

J.-B. Wang, N. He, L.-L. Zhang, and K. Lu, “Single image dehazing with a physical model and dark channel prior,” Neurocomputing 149, 718–728 (2015).
[Crossref]

Mackiewicz, M.

G. D. Finlayson, M. M. Darrodi, and M. Mackiewicz, “The alternating least squares technique for nonuniform intensity color correction,” Color Res. Appl. 40(3), 232–242 (2015).
[Crossref]

Mai, J.

Q. Zhu, J. Mai, and L. Shao, “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior,” IEEE Transactions on Image Process. 24(11), 3522–3533 (2015).
[Crossref]

Matlin, E.

E. Matlin and P. Milanfar, “Removal of haze and noise from a single image,” Proc. SPIE 8296, 82960T (2012).
[Crossref]

Meng, G.

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient Image Dehazing with Boundary Constraint and Contextual Regularization,” in Proceedings of IEEE International Conference on Computer Vision (ICCV), (IEEE, 2013), pp. 617–624.

Milanfar, P.

E. Matlin and P. Milanfar, “Removal of haze and noise from a single image,” Proc. SPIE 8296, 82960T (2012).
[Crossref]

Mittal, A.

A. Mittal, R. Soundararajan, and A. Bovik, “Making a completely blind image quality analyzer,” Signal Process. Lett. IEEE 20(3), 209–212 (2013).
[Crossref]

A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on Image Process. 21(12), 4695–4708 (2012).
[Crossref]

Moorthy, A. K.

A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on Image Process. 21(12), 4695–4708 (2012).
[Crossref]

Morel, J.-M.

A. B. Petro, C. Sbert, and J.-M. Morel, “Multiscale Retinex,” Image Processing On Line 4, 71–88 (2014).
[Crossref]

Nishino, K.

K. Nishino, L. Kratz, and S. Lombardi, “Bayesian Defogging,” Int. J. Comput. Vis. 98(3), 263–278 (2012).
[Crossref]

Oakley, J.

J. Oakley and H. Bu, “Correction of Simple Contrast Loss in Color Images,” IEEE Transactions on Image Process. 16(2), 511–522 (2007).
[Crossref]

Paik, J.

Pan, C.

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient Image Dehazing with Boundary Constraint and Contextual Regularization,” in Proceedings of IEEE International Conference on Computer Vision (ICCV), (IEEE, 2013), pp. 617–624.

Pardo, D.

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Fusion-based variational image dehazing,” IEEE Signal Process. Lett. 24, 1 (2016).
[Crossref]

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Enhanced Variational Image Dehazing,” SIAM J. on Imaging Sci. 8(3), 1519–1546 (2015).
[Crossref]

Patel, V. M.

H. Zhang and V. M. Patel, “Densely connected pyramid dehazing network,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2018), pp. 3194–3203.

Pelli, D. G.

D. G. Pelli, “The VideoToolbox software for visual psychophysics: transforming numbers into movies,” Spat Vis. 10(4), 437–442 (1997).
[Crossref]

Petro, A. B.

A. B. Petro, C. Sbert, and J.-M. Morel, “Multiscale Retinex,” Image Processing On Line 4, 71–88 (2014).
[Crossref]

Preiss, J.

I. Lissner, J. Preiss, P. Urban, M. S. Lichtenauer, and P. Zolliker, “Image-difference prediction: From grayscale to color,” IEEE Transactions on Image Process. 22(2), 435–446 (2013).
[Crossref]

Qing, C.

B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “DehazeNet: An End-to-End System for Single Image Haze Removal,” arXiv:1601.07661 (2016).

Sbert, C.

A. B. Petro, C. Sbert, and J.-M. Morel, “Multiscale Retinex,” Image Processing On Line 4, 71–88 (2014).
[Crossref]

Shao, L.

Q. Zhu, J. Mai, and L. Shao, “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior,” IEEE Transactions on Image Process. 24(11), 3522–3533 (2015).
[Crossref]

Sheikh, H. R.

H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Transactions on Image Process. 15(2), 430–444 (2006).
[Crossref]

Soundararajan, R.

A. Mittal, R. Soundararajan, and A. Bovik, “Making a completely blind image quality analyzer,” Signal Process. Lett. IEEE 20(3), 209–212 (2013).
[Crossref]

Sun, J.

K. He, J. Sun, and X. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Transactions on Pattern Analysis Mach. Intell. 33(12), 2341–2353 (2011).
[Crossref]

Sun, W.

W. Sun, “A new single-image fog removal algorithm based on physical model,” Optik 124(21), 4770–4775 (2013).
[Crossref]

Tan, R.

R. Tan, “Visibility in bad weather from a single image,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2008), pp. 1–8.

Tan, R. T.

Y. Li, F. Guo, R. T. Tan, and M. S. Brown, “A contrast enhancement framework with jpeg artifacts suppression,” in Computer Vision - ECCV 2014 - 13th European Conference, (2014), pp. 174–188.

Tang, K.

K. Tang, J. Yang, and J. Wang, “Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2014), pp. 2995–3002.

Tang, X.

K. He, J. Sun, and X. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Transactions on Pattern Analysis Mach. Intell. 33(12), 2341–2353 (2011).
[Crossref]

Tao, D.

B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “DehazeNet: An End-to-End System for Single Image Haze Removal,” arXiv:1601.07661 (2016).

Tarel, J.-P.

J.-P. Tarel and N. Hautiere, “Fast visibility restoration from a single color or gray level image,” in Proceedings of IEEE International Conference on Computer Vision, (IEEE, 2009), pp. 2201–2208.

Urban, P.

I. Lissner, J. Preiss, P. Urban, M. S. Lichtenauer, and P. Zolliker, “Image-difference prediction: From grayscale to color,” IEEE Transactions on Image Process. 22(2), 435–446 (2013).
[Crossref]

Vazquez-Corral, J.

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Fusion-based variational image dehazing,” IEEE Signal Process. Lett. 24, 1 (2016).
[Crossref]

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Enhanced Variational Image Dehazing,” SIAM J. on Imaging Sci. 8(3), 1519–1546 (2015).
[Crossref]

A. Galdran, A. Alvarez-Gila, A. Bria, J. Vazquez-Corral, and M. Bertalmío, “On the duality between retinex and image dehazing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2018).

J. Vazquez-Corral, A. Galdran, P. Cyriac, and M. Bertalmío, “A fast image dehazing method that does not introduce color artifacts,” Journal of Real-Time Image Processing, pp. doi: 10.1007/s11554–018–0816–6 posted 29 August 2018, in press.

J. Vazquez-Corral, G. D. Finlayson, and M. Bertalmío, “Physically plausible dehazing for non-physical dehazing algorithms,” in Computational Color Imaging, S. Tominaga, R. Schettini, A. Trémeau, and T. Horiuchi, eds. (Springer International Publishing, 2019), pp. 233–244.

Vleeschouwer, C. D.

C. Ancuti, C. O. Ancuti, and C. D. Vleeschouwer, “D-hazy: A dataset to evaluate quantitatively dehazing algorithms,” in Proceedings of the IEEE International Conference on Image Processing, (IEEE, 2016), CIP’16.

Wang, H.

Y. Wang, H. Wang, C. Yin, and M. Dai, “Biologically inspired image enhancement based on Retinex,” Neurocomputing 177, 373–384 (2016).
[Crossref]

Wang, J.

C. Chen, M. N. Do, and J. Wang, “Robust image and video dehazing with visual artifact suppression via gradient residual minimization, in Computer Vision - ECCV 2016 - 14th European Conference, (2016), pp. 576–591.

K. Tang, J. Yang, and J. Wang, “Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2014), pp. 2995–3002.

Wang, J.-B.

J.-B. Wang, N. He, L.-L. Zhang, and K. Lu, “Single image dehazing with a physical model and dark channel prior,” Neurocomputing 149, 718–728 (2015).
[Crossref]

Wang, S.

S. Wang, W. Cho, J. Jang, M. A. Abidi, and J. Paik, “Contrast-dependent saturation adjustment for outdoor image enhancement,” J. Opt. Soc. Am. A 34(1), 7–17 (2017).
[Crossref]

Y. Gao, H.-M. Hu, S. Wang, and B. Li, “A fast image dehazing algorithm based on negative correction,” Signal Process. 103, 380–398 (2014).
[Crossref]

Wang, Y.

Y. Wang, H. Wang, C. Yin, and M. Dai, “Biologically inspired image enhancement based on Retinex,” Neurocomputing 177, 373–384 (2016).
[Crossref]

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient Image Dehazing with Boundary Constraint and Contextual Regularization,” in Proceedings of IEEE International Conference on Computer Vision (ICCV), (IEEE, 2013), pp. 617–624.

Xiang, S.

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient Image Dehazing with Boundary Constraint and Contextual Regularization,” in Proceedings of IEEE International Conference on Computer Vision (ICCV), (IEEE, 2013), pp. 617–624.

Xu, X.

B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “DehazeNet: An End-to-End System for Single Image Haze Removal,” arXiv:1601.07661 (2016).

Yang, J.

K. Tang, J. Yang, and J. Wang, “Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2014), pp. 2995–3002.

Yin, C.

Y. Wang, H. Wang, C. Yin, and M. Dai, “Biologically inspired image enhancement based on Retinex,” Neurocomputing 177, 373–384 (2016).
[Crossref]

You, J.

L. K. Choi, J. You, and A. C. Bovik, “Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging,” IEEE Transactions on Image Process. 24(11), 3888–3901 (2015).
[Crossref]

Zeng, D.

X. Fu, D. Zeng, Y. Huang, X. P. Zhang, and X. Ding, “A weighted variational model for simultaneous reflectance and illumination estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 2782–2790.

Zhang, H.

H. Zhang and V. M. Patel, “Densely connected pyramid dehazing network,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2018), pp. 3194–3203.

Zhang, L.-L.

J.-B. Wang, N. He, L.-L. Zhang, and K. Lu, “Single image dehazing with a physical model and dark channel prior,” Neurocomputing 149, 718–728 (2015).
[Crossref]

Zhang, X. P.

X. Fu, D. Zeng, Y. Huang, X. P. Zhang, and X. Ding, “A weighted variational model for simultaneous reflectance and illumination estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 2782–2790.

Zhang, X.-S.

X.-S. Zhang, S.-B. Gao, C.-Y. Li, and Y.-J. Li, “A Retina Inspired Model for Enhancing Visibility of Hazy Images,” Front. Comput. Neurosci. 9, 151 (2015).
[Crossref]

Zheng, J.

Z. Li and J. Zheng, “Edge-Preserving Decomposition-Based Single Image Haze Removal,” IEEE Transactions on Image Process. 24(12), 5432–5441 (2015).
[Crossref]

Zhu, Q.

Q. Zhu, J. Mai, and L. Shao, “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior,” IEEE Transactions on Image Process. 24(11), 3522–3533 (2015).
[Crossref]

Zolliker, P.

I. Lissner, J. Preiss, P. Urban, M. S. Lichtenauer, and P. Zolliker, “Image-difference prediction: From grayscale to color,” IEEE Transactions on Image Process. 22(2), 435–446 (2013).
[Crossref]

Color Res. Appl. (1)

G. D. Finlayson, M. M. Darrodi, and M. Mackiewicz, “The alternating least squares technique for nonuniform intensity color correction,” Color Res. Appl. 40(3), 232–242 (2015).
[Crossref]

Front. Comput. Neurosci. (1)

X.-S. Zhang, S.-B. Gao, C.-Y. Li, and Y.-J. Li, “A Retina Inspired Model for Enhancing Visibility of Hazy Images,” Front. Comput. Neurosci. 9, 151 (2015).
[Crossref]

IEEE Signal Process. Lett. (1)

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Fusion-based variational image dehazing,” IEEE Signal Process. Lett. 24, 1 (2016).
[Crossref]

IEEE Transactions on Circuits Syst. for Video Technol. (1)

Y.-H. Lai, Y.-L. Chen, C.-J. Chiou, and C.-T. Hsu, “Single-Image Dehazing via Optimal Transmission Map Under Scene Priors,” IEEE Transactions on Circuits Syst. for Video Technol. 25(1), 1–14 (2015).

IEEE Transactions on Image Process. (8)

Q. Zhu, J. Mai, and L. Shao, “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior,” IEEE Transactions on Image Process. 24(11), 3522–3533 (2015).
[Crossref]

Z. Li and J. Zheng, “Edge-Preserving Decomposition-Based Single Image Haze Removal,” IEEE Transactions on Image Process. 24(12), 5432–5441 (2015).
[Crossref]

J. Oakley and H. Bu, “Correction of Simple Contrast Loss in Color Images,” IEEE Transactions on Image Process. 16(2), 511–522 (2007).
[Crossref]

C. Ancuti and C. Ancuti, “Single Image Dehazing by Multi-Scale Fusion,” IEEE Transactions on Image Process. 22(8), 3271–3282 (2013).
[Crossref]

L. K. Choi, J. You, and A. C. Bovik, “Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging,” IEEE Transactions on Image Process. 24(11), 3888–3901 (2015).
[Crossref]

I. Lissner, J. Preiss, P. Urban, M. S. Lichtenauer, and P. Zolliker, “Image-difference prediction: From grayscale to color,” IEEE Transactions on Image Process. 22(2), 435–446 (2013).
[Crossref]

H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Transactions on Image Process. 15(2), 430–444 (2006).
[Crossref]

A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on Image Process. 21(12), 4695–4708 (2012).
[Crossref]

IEEE Transactions on Pattern Analysis Mach. Intell. (1)

K. He, J. Sun, and X. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Transactions on Pattern Analysis Mach. Intell. 33(12), 2341–2353 (2011).
[Crossref]

Image Processing On Line (1)

A. B. Petro, C. Sbert, and J.-M. Morel, “Multiscale Retinex,” Image Processing On Line 4, 71–88 (2014).
[Crossref]

Int. J. Comput. Vis. (1)

K. Nishino, L. Kratz, and S. Lombardi, “Bayesian Defogging,” Int. J. Comput. Vis. 98(3), 263–278 (2012).
[Crossref]

J. Opt. Soc. Am. A (1)

Neurocomputing (2)

J.-B. Wang, N. He, L.-L. Zhang, and K. Lu, “Single image dehazing with a physical model and dark channel prior,” Neurocomputing 149, 718–728 (2015).
[Crossref]

Y. Wang, H. Wang, C. Yin, and M. Dai, “Biologically inspired image enhancement based on Retinex,” Neurocomputing 177, 373–384 (2016).
[Crossref]

Optik (1)

W. Sun, “A new single-image fog removal algorithm based on physical model,” Optik 124(21), 4770–4775 (2013).
[Crossref]

Proc. SPIE (1)

E. Matlin and P. Milanfar, “Removal of haze and noise from a single image,” Proc. SPIE 8296, 82960T (2012).
[Crossref]

SIAM J. on Imaging Sci. (1)

A. Galdran, J. Vazquez-Corral, D. Pardo, and M. Bertalmío, “Enhanced Variational Image Dehazing,” SIAM J. on Imaging Sci. 8(3), 1519–1546 (2015).
[Crossref]

Signal Process. (1)

Y. Gao, H.-M. Hu, S. Wang, and B. Li, “A fast image dehazing algorithm based on negative correction,” Signal Process. 103, 380–398 (2014).
[Crossref]

Signal Process. Lett. IEEE (1)

A. Mittal, R. Soundararajan, and A. Bovik, “Making a completely blind image quality analyzer,” Signal Process. Lett. IEEE 20(3), 209–212 (2013).
[Crossref]

Spat Vis. (2)

D. H. Brainard, “The Psychophysics Toolbox,” Spat Vis. 10(4), 433–436 (1997).
[Crossref]

D. G. Pelli, “The VideoToolbox software for visual psychophysics: transforming numbers into movies,” Spat Vis. 10(4), 437–442 (1997).
[Crossref]

Other (17)

C. Ancuti, C. O. Ancuti, and C. D. Vleeschouwer, “D-hazy: A dataset to evaluate quantitatively dehazing algorithms,” in Proceedings of the IEEE International Conference on Image Processing, (IEEE, 2016), CIP’16.

X. Fu, D. Zeng, Y. Huang, X. P. Zhang, and X. Ding, “A weighted variational model for simultaneous reflectance and illumination estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 2782–2790.

Y. Li, F. Guo, R. T. Tan, and M. S. Brown, “A contrast enhancement framework with jpeg artifacts suppression,” in Computer Vision - ECCV 2014 - 13th European Conference, (2014), pp. 174–188.

C. Chen, M. N. Do, and J. Wang, “Robust image and video dehazing with visual artifact suppression via gradient residual minimization, in Computer Vision - ECCV 2016 - 14th European Conference, (2016), pp. 576–591.

V. De Dravo and J. Hardeberg, “Stress for dehazing, in Colour and Visual Computing Symposium (CVCS), 2015, (2015), pp. 1–6.

C. O. Ancuti, C. Ancuti, C. Hermans, and P. Bekaert, “A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image, in Asian Conference on Computer Vision, ACCV-2010, (2010) 6493, pp. 501–514.

J. Vazquez-Corral, A. Galdran, P. Cyriac, and M. Bertalmío, “A fast image dehazing method that does not introduce color artifacts,” Journal of Real-Time Image Processing, pp. doi: 10.1007/s11554–018–0816–6 posted 29 August 2018, in press.

A. Galdran, A. Alvarez-Gila, A. Bria, J. Vazquez-Corral, and M. Bertalmío, “On the duality between retinex and image dehazing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2018).

B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “DehazeNet: An End-to-End System for Single Image Haze Removal,” arXiv:1601.07661 (2016).

H. Zhang and V. M. Patel, “Densely connected pyramid dehazing network,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2018), pp. 3194–3203.

K. Tang, J. Yang, and J. Wang, “Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2014), pp. 2995–3002.

J.-P. Tarel and N. Hautiere, “Fast visibility restoration from a single color or gray level image,” in Proceedings of IEEE International Conference on Computer Vision, (IEEE, 2009), pp. 2201–2208.

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient Image Dehazing with Boundary Constraint and Contextual Regularization,” in Proceedings of IEEE International Conference on Computer Vision (ICCV), (IEEE, 2013), pp. 617–624.

H. Koschmieder, Theorie der horizontalen Sichtweite: Kontrast und Sichtweite (Keim & Nemnich, 1925).

J. Vazquez-Corral, G. D. Finlayson, and M. Bertalmío, “Physically plausible dehazing for non-physical dehazing algorithms,” in Computational Color Imaging, S. Tominaga, R. Schettini, A. Trémeau, and T. Horiuchi, eds. (Springer International Publishing, 2019), pp. 233–244.

R. Tan, “Visibility in bad weather from a single image,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2008), pp. 1–8.

R. Fattal, “Single Image Dehazing,” in ACM SIGGRAPH 2008 Papers, (ACM, 2008), SIGGRAPH ’08, pp. 72:1–72:9.

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Figures (5)

Fig. 1.
Fig. 1. Study about the effect of the iterations on the steady state of $\boldsymbol {J^{our}}$ for different original algorithms in the 500 images of the dataset in Choi et al.. We can clearly see that for any original algorithm $\boldsymbol {J^{our}}$ reaches steady state.
Fig. 2.
Fig. 2. Qualitative results for our approach, for 6 different non-physical dehazing methods and 2 different starting airlights. Our method improves all the original methods. Furthermore, our results for both airlights are very similar, showing the robustness of our approach.
Fig. 3.
Fig. 3. Images uses in the psychophysical experiment.
Fig. 4.
Fig. 4. Results of the psychophysical experiment using the Thurstone Case V test for the whole set of $150$ comparisons.
Fig. 5.
Fig. 5. Results of the psychophysical experiment using the Thurstone Case V test for each of the non-physical dehazing methods considered in this work.

Tables (2)

Tables Icon

Table 1. Results reported as the mean for all the 500 images in the Choi et al. dataset.

Tables Icon

Table 2. Results reported as the mean for all the 23 images in the Middleburry D-Hazy dataset.

Equations (10)

Equations on this page are rendered with MathJax. Learn more.

I x , = t x J x , + ( 1 t x ) A ,
{ A o u r , t o u r } = a r g m i n A , t ( 1 t ) A I + T J n p 2 ,
t o u r = d i a g ( T ) | T = a r g m i n T ( I Λ ) T ( J n p Λ ) 2 .
T ( x , y ) = a r g m i n T ( x , y ) ( I Λ _ ) ( x , y ) T ( x , y ) ( J n p Λ _ ) ( x , y ) 2 , s . t . T ( x , y ) = k = 1 K α k G k ( x , y ) .
H = [ H r , 1 H r , K H g , 1 H g , K H b , 1 H b , K ] .
u = [ ( I Λ ) r ( I Λ ) g ( I Λ ) b ] .
α = H + u
A o u r = a r g m i n A ( 1 t o u r ) A I T o u r J n p 2 .
J x , o u r = I x , o r ( 1 t x o u r ) A o u r t x o u r
M S E ( k ) = 1 3 N j = 1 3 i = 1 N ( J i , j o u r ( k + 1 ) J i , j o u r ( k ) ) 2 ,

Metrics