Abstract

Photographic images taken in foggy or hazy weather (hazy images) exhibit poor visibility and detail because of scattering and attenuation of light caused by suspended particles, and therefore, image dehazing has attracted considerable research attention. The current polarization-based dehazing algorithms strongly rely on the presence of a “sky area”, and thus, the selection of model parameters is susceptible to external interference of high-brightness objects and strong light sources. In addition, the noise of the restored image is large. In order to solve these problems, we propose a polarization-based dehazing algorithm that does not rely on the sky area (“non-sky”). First, a linear polarizer is used to collect three polarized images. The maximum- and minimum-intensity images are then obtained by calculation, assuming the polarization of light emanating from objects is negligible in most scenarios involving non-specular objects. Subsequently, the polarization difference of the two images is used to determine a sky area and calculate the infinite atmospheric light value. Next, using the global features of the image, and based on the assumption that the airlight and object radiance are irrelevant, the degree of polarization of the airlight (DPA) is calculated by solving for the optimal solution of the correlation coefficient equation between airlight and object radiance; the optimal solution is obtained by setting the right-hand side of the equation to zero. Then, the hazy image is subjected to dehazing. Subsequently, a filtering denoising algorithm, which combines the polarization difference information and block-matching and 3D (BM3D) filtering, is designed to filter the image smoothly. Our experimental results show that the proposed polarization-based dehazing algorithm does not depend on whether the image includes a sky area and does not require complex models. Moreover, the dehazing image except specular object scenarios is superior to those obtained by Tarel, Fattal, Ren, and Berman based on the criteria of no-reference quality assessment (NRQA), blind/referenceless image spatial quality evaluator (BRISQUE), blind anistropic quality index (AQI), and e.

© 2017 Optical Society of America

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References

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    [Crossref]

2015 (3)

Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Transactions on Image Processing 24, 3522–3533 (2015).
[Crossref] [PubMed]

W. Sun, H. Wang, C. Sun, B. Guo, W. Jia, and M. Sun, “Fast single image haze removal via local atmospheric light veil estimation,” Computers & Electrical Engineering 46, 371–383 (2015).
[Crossref]

J. Liang, L. Ren, H. Ju, W. Zhang, and E. Qu, “Polarimetric dehazing method for dense haze removal based on distribution analysis of angle of polarization,” Opt. Express 23, 26146–26157 (2015).
[Crossref] [PubMed]

2013 (1)

C. O. Ancuti and C. Ancuti, “Single image dehazing by multi-scale fusion,” IEEE Transactions on Image Processing 22, 3271–3282 (2013).
[Crossref] [PubMed]

2012 (1)

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

2011 (1)

N. Hautière, J. P. Tarel, D. Aubert, and E. Dumont, “Blind contrast enhancement assessment by gradient ratioing at visible edges,” Image Analysis & Stereology 27, 87–95 (2011).
[Crossref]

2010 (1)

K.M. He, J. Sun, and X.O. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010).
[PubMed]

2009 (1)

2008 (2)

R. Fattal, “Single image dehazing,” ACM Trans.Graph. 27(3), 988–992 (2008).

J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: Model-based photograph enhancement and viewing,” ACM Trans. Graph. 27(5), 1–10 (2008).
[Crossref]

2007 (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Trans. on Image Processing 16, 2080–2095 (2007).
[Crossref]

2006 (1)

2005 (1)

H. R. Sheikh, A. C. Bovik, and L. Cormack, “No-reference quality assessment using natural scene statistics: Jpeg2000,” IEEE Transactions on Image Processing 14, 1918–1927 (2005).
[Crossref] [PubMed]

2003 (2)

2002 (1)

2001 (1)

1998 (2)

J. Tyo, “Optimum linear combination strategy for an n-channel polarization-sensitive imaging or vision system,” J. Opt. Soc. Am. A 15(2), 359–366 (1998).
[Crossref]

J. P. Oakley and B. L. Satherley, “Improving image quality in poor visibility conditions using a physical model for contrast degradation,” IEEE Trans. Image Process. 7(2), 167–179 (1998).
[Crossref]

1996 (2)

H. Chen and L. B. Wolff, “Polarization phase-based method for material classification and object recognition in computer vision,” Proc. SPIE 2599, 54–63 (1996).
[Crossref]

J. Tyo, M. Rowe, E. Pugh, and N. Engheta, “Target detection in optically scattering media by polarization-difference imaging,” Appl. Opt. 35(11), 1855–1870 (1996).
[Crossref] [PubMed]

Ancuti, C.

C. O. Ancuti and C. Ancuti, “Single image dehazing by multi-scale fusion,” IEEE Transactions on Image Processing 22, 3271–3282 (2013).
[Crossref] [PubMed]

Ancuti, C. O.

C. O. Ancuti and C. Ancuti, “Single image dehazing by multi-scale fusion,” IEEE Transactions on Image Processing 22, 3271–3282 (2013).
[Crossref] [PubMed]

Astola, J.

N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, “Color image database tid2013: Peculiarities and preliminary results,” In Proc. of European Workshop on Visual Information Processing, pp. 106–111, Paris, France (2013).

Aubert, D.

N. Hautière, J. P. Tarel, D. Aubert, and E. Dumont, “Blind contrast enhancement assessment by gradient ratioing at visible edges,” Image Analysis & Stereology 27, 87–95 (2011).
[Crossref]

N. Hautière, J.P. Tarel, and D. Aubert, “Towards fog-free in-vehicle vision systems through contrast restoration,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

Avidan, S.

D. Berman, T. treibitz, and S. Avidan, “Non-local image dehazing,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016).

Bass, M.

M. Bass, Devices, Measurements, and Properties, Vol. 2 of Handbook of Optics (McGraw-Hill, 1995), Chap. 22.

Battisti, F.

N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, “Color image database tid2013: Peculiarities and preliminary results,” In Proc. of European Workshop on Visual Information Processing, pp. 106–111, Paris, France (2013).

Ben-Ezra, M.

M. Ben-Ezra, “Segmentation with invisible keying signal,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), pp. 32–37.

Berman, D.

D. Berman, T. treibitz, and S. Avidan, “Non-local image dehazing,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016).

Bovik, A. C.

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

H. R. Sheikh, A. C. Bovik, and L. Cormack, “No-reference quality assessment using natural scene statistics: Jpeg2000,” IEEE Transactions on Image Processing 14, 1918–1927 (2005).
[Crossref] [PubMed]

A. K. Moorthy and A. C. Bovik, “A two-step framework for constructing blind image quality indices,” in Proceedings of IEEE Conference on Signal Processing Letters (IEEE, 2010) pp. 513–516.
[Crossref]

A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” in Proceedings of IEEE Conference on Signal Processing Letters (IEEE, 2013), pp. 209–212.
[Crossref]

Cao, X.

W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M. H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in European Conference on Computer Vision (Springer, 2016), pp. 154–169.

Carli, M.

N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, “Color image database tid2013: Peculiarities and preliminary results,” In Proc. of European Workshop on Visual Information Processing, pp. 106–111, Paris, France (2013).

Chehdi, K.

N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, “Color image database tid2013: Peculiarities and preliminary results,” In Proc. of European Workshop on Visual Information Processing, pp. 106–111, Paris, France (2013).

Chen, B.

J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: Model-based photograph enhancement and viewing,” ACM Trans. Graph. 27(5), 1–10 (2008).
[Crossref]

Chen, H.

H. Chen and L. B. Wolff, “Polarization phase-based method for material classification and object recognition in computer vision,” Proc. SPIE 2599, 54–63 (1996).
[Crossref]

Cohen, M.

J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: Model-based photograph enhancement and viewing,” ACM Trans. Graph. 27(5), 1–10 (2008).
[Crossref]

Cohen-Or, D.

J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: Model-based photograph enhancement and viewing,” ACM Trans. Graph. 27(5), 1–10 (2008).
[Crossref]

Cormack, L.

H. R. Sheikh, A. C. Bovik, and L. Cormack, “No-reference quality assessment using natural scene statistics: Jpeg2000,” IEEE Transactions on Image Processing 14, 1918–1927 (2005).
[Crossref] [PubMed]

Dabov, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Trans. on Image Processing 16, 2080–2095 (2007).
[Crossref]

Deussen, O.

J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: Model-based photograph enhancement and viewing,” ACM Trans. Graph. 27(5), 1–10 (2008).
[Crossref]

Dumont, E.

N. Hautière, J. P. Tarel, D. Aubert, and E. Dumont, “Blind contrast enhancement assessment by gradient ratioing at visible edges,” Image Analysis & Stereology 27, 87–95 (2011).
[Crossref]

Egiazarian, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Trans. on Image Processing 16, 2080–2095 (2007).
[Crossref]

N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, “Color image database tid2013: Peculiarities and preliminary results,” In Proc. of European Workshop on Visual Information Processing, pp. 106–111, Paris, France (2013).

Engheta, N.

Fattal, R.

R. Fattal, “Single image dehazing,” ACM Trans.Graph. 27(3), 988–992 (2008).

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Trans. on Image Processing 16, 2080–2095 (2007).
[Crossref]

Guo, B.

W. Sun, H. Wang, C. Sun, B. Guo, W. Jia, and M. Sun, “Fast single image haze removal via local atmospheric light veil estimation,” Computers & Electrical Engineering 46, 371–383 (2015).
[Crossref]

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 (Kyoto, Japan, 2009), pp. 2201–2208.

Hautière, N.

N. Hautière, J. P. Tarel, D. Aubert, and E. Dumont, “Blind contrast enhancement assessment by gradient ratioing at visible edges,” Image Analysis & Stereology 27, 87–95 (2011).
[Crossref]

N. Hautière, J.P. Tarel, and D. Aubert, “Towards fog-free in-vehicle vision systems through contrast restoration,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

He, K.M.

K.M. He, J. Sun, and X.O. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010).
[PubMed]

Ieremeiev, O.

N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, “Color image database tid2013: Peculiarities and preliminary results,” In Proc. of European Workshop on Visual Information Processing, pp. 106–111, Paris, France (2013).

Ikeuchi, K.

M. Saito, Y. Sato, K. Ikeuchi, and H. Kashiwagi, “Measurement of surface orientations of transparent objects by use of polarization in highlight,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1999), pp. 381–386.

Jia, W.

W. Sun, H. Wang, C. Sun, B. Guo, W. Jia, and M. Sun, “Fast single image haze removal via local atmospheric light veil estimation,” Computers & Electrical Engineering 46, 371–383 (2015).
[Crossref]

Jin, L.

N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, “Color image database tid2013: Peculiarities and preliminary results,” In Proc. of European Workshop on Visual Information Processing, pp. 106–111, Paris, France (2013).

Ju, H.

Kashiwagi, H.

M. Saito, Y. Sato, K. Ikeuchi, and H. Kashiwagi, “Measurement of surface orientations of transparent objects by use of polarization in highlight,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1999), pp. 381–386.

Katkovnik, V.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Trans. on Image Processing 16, 2080–2095 (2007).
[Crossref]

Kopf, J.

J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: Model-based photograph enhancement and viewing,” ACM Trans. Graph. 27(5), 1–10 (2008).
[Crossref]

Kratz, L.

L. Kratz and K. Nishino, “Factorizing scene albedo and depth from a single foggy image,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), pp. 1701–1708.

Kwok, N.

S. Liu, M. A. Rahman, C. Y. Wong, C. F. Lin, H. Wu, and N. Kwok, “Image dehazing from the perspective of noise filtering,” Computers & Electrical Engineering, in press (2016).
[Crossref]

Liang, J.

Lin, C. F.

S. Liu, M. A. Rahman, C. Y. Wong, C. F. Lin, H. Wu, and N. Kwok, “Image dehazing from the perspective of noise filtering,” Computers & Electrical Engineering, in press (2016).
[Crossref]

Lin, S. S.

Lischinski, D.

J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: Model-based photograph enhancement and viewing,” ACM Trans. Graph. 27(5), 1–10 (2008).
[Crossref]

Liu, S.

W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M. H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in European Conference on Computer Vision (Springer, 2016), pp. 154–169.

S. Liu, M. A. Rahman, C. Y. Wong, C. F. Lin, H. Wu, and N. Kwok, “Image dehazing from the perspective of noise filtering,” Computers & Electrical Engineering, in press (2016).
[Crossref]

Lo, M.

Lukin, V.

N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, “Color image database tid2013: Peculiarities and preliminary results,” In Proc. of European Workshop on Visual Information Processing, pp. 106–111, Paris, France (2013).

Mai, J.

Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Transactions on Image Processing 24, 3522–3533 (2015).
[Crossref] [PubMed]

Mittal, A.

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

A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” in Proceedings of IEEE Conference on Signal Processing Letters (IEEE, 2013), pp. 209–212.
[Crossref]

Moorthy, A. K.

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

A. K. Moorthy and A. C. Bovik, “A two-step framework for constructing blind image quality indices,” in Proceedings of IEEE Conference on Signal Processing Letters (IEEE, 2010) pp. 513–516.
[Crossref]

Namer, E.

Narasimhan, S. G.

Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, “Polarization-based vision through haze,” Appl. Opt. 42(3), 511–525 (2003).
[Crossref] [PubMed]

S. G. Narasimhan and S. K. Nayar, “Interactive (de) weathering of an image using physical models,” Proceedings of IEEE Workshop Color and Photometric Methods in Computer Vision (2003).

Nayar, S. K.

Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, “Polarization-based vision through haze,” Appl. Opt. 42(3), 511–525 (2003).
[Crossref] [PubMed]

S. G. Narasimhan and S. K. Nayar, “Interactive (de) weathering of an image using physical models,” Proceedings of IEEE Workshop Color and Photometric Methods in Computer Vision (2003).

Neubert, B.

J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: Model-based photograph enhancement and viewing,” ACM Trans. Graph. 27(5), 1–10 (2008).
[Crossref]

Nishino, K.

L. Kratz and K. Nishino, “Factorizing scene albedo and depth from a single foggy image,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), pp. 1701–1708.

Oakley, J. P.

K. K. Tan and J. P. Oakley, “Physics-based approach to color image enhancement in poor visibility conditions,” J. Opt. Soc. Am. A 18(10), 2460–2467 (2001).
[Crossref]

J. P. Oakley and B. L. Satherley, “Improving image quality in poor visibility conditions using a physical model for contrast degradation,” IEEE Trans. Image Process. 7(2), 167–179 (1998).
[Crossref]

Pan, J.

W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M. H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in European Conference on Computer Vision (Springer, 2016), pp. 154–169.

Ponomarenko, N.

N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, “Color image database tid2013: Peculiarities and preliminary results,” In Proc. of European Workshop on Visual Information Processing, pp. 106–111, Paris, France (2013).

Pugh, E.

Pugh, E. N.

Qu, E.

Rahman, M. A.

S. Liu, M. A. Rahman, C. Y. Wong, C. F. Lin, H. Wu, and N. Kwok, “Image dehazing from the perspective of noise filtering,” Computers & Electrical Engineering, in press (2016).
[Crossref]

Ren, L.

Ren, W.

W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M. H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in European Conference on Computer Vision (Springer, 2016), pp. 154–169.

Rowe, M.

Saito, M.

M. Saito, Y. Sato, K. Ikeuchi, and H. Kashiwagi, “Measurement of surface orientations of transparent objects by use of polarization in highlight,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1999), pp. 381–386.

Satherley, B. L.

J. P. Oakley and B. L. Satherley, “Improving image quality in poor visibility conditions using a physical model for contrast degradation,” IEEE Trans. Image Process. 7(2), 167–179 (1998).
[Crossref]

Sato, Y.

M. Saito, Y. Sato, K. Ikeuchi, and H. Kashiwagi, “Measurement of surface orientations of transparent objects by use of polarization in highlight,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1999), pp. 381–386.

Schechner, Y. Y.

Shao, L.

Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Transactions on Image Processing 24, 3522–3533 (2015).
[Crossref] [PubMed]

Sheikh, H. R.

H. R. Sheikh, A. C. Bovik, and L. Cormack, “No-reference quality assessment using natural scene statistics: Jpeg2000,” IEEE Transactions on Image Processing 14, 1918–1927 (2005).
[Crossref] [PubMed]

Shwartz, S.

Soundararajan, R.

A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” in Proceedings of IEEE Conference on Signal Processing Letters (IEEE, 2013), pp. 209–212.
[Crossref]

Sun, C.

W. Sun, H. Wang, C. Sun, B. Guo, W. Jia, and M. Sun, “Fast single image haze removal via local atmospheric light veil estimation,” Computers & Electrical Engineering 46, 371–383 (2015).
[Crossref]

Sun, J.

K.M. He, J. Sun, and X.O. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010).
[PubMed]

Sun, M.

W. Sun, H. Wang, C. Sun, B. Guo, W. Jia, and M. Sun, “Fast single image haze removal via local atmospheric light veil estimation,” Computers & Electrical Engineering 46, 371–383 (2015).
[Crossref]

Sun, W.

W. Sun, H. Wang, C. Sun, B. Guo, W. Jia, and M. Sun, “Fast single image haze removal via local atmospheric light veil estimation,” Computers & Electrical Engineering 46, 371–383 (2015).
[Crossref]

Tan, K. K.

Tan, R. T.

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

Tang, K.

K. Tang, J. Yang, and J. Wang, “Investigating haze-relevant features in a learning framework for image dehazing,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2014).

Tang, X.O.

K.M. He, J. Sun, and X.O. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010).
[PubMed]

Tarel, J. P.

N. Hautière, J. P. Tarel, D. Aubert, and E. Dumont, “Blind contrast enhancement assessment by gradient ratioing at visible edges,” Image Analysis & Stereology 27, 87–95 (2011).
[Crossref]

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 (Kyoto, Japan, 2009), pp. 2201–2208.

Tarel, J.P.

N. Hautière, J.P. Tarel, and D. Aubert, “Towards fog-free in-vehicle vision systems through contrast restoration,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

treibitz, T.

D. Berman, T. treibitz, and S. Avidan, “Non-local image dehazing,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016).

Tyo, J.

Tyo, J. S.

Uyttendaele, M.

J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: Model-based photograph enhancement and viewing,” ACM Trans. Graph. 27(5), 1–10 (2008).
[Crossref]

Vozel, B.

N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, “Color image database tid2013: Peculiarities and preliminary results,” In Proc. of European Workshop on Visual Information Processing, pp. 106–111, Paris, France (2013).

Wang, H.

W. Sun, H. Wang, C. Sun, B. Guo, W. Jia, and M. Sun, “Fast single image haze removal via local atmospheric light veil estimation,” Computers & Electrical Engineering 46, 371–383 (2015).
[Crossref]

Wang, J.

K. Tang, J. Yang, and J. Wang, “Investigating haze-relevant features in a learning framework for image dehazing,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2014).

Wolff, L. B.

H. Chen and L. B. Wolff, “Polarization phase-based method for material classification and object recognition in computer vision,” Proc. SPIE 2599, 54–63 (1996).
[Crossref]

L. B. Wolff, “Using polarization to separate reflection components,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1989), pp. 363–369.
[Crossref]

Wong, C. Y.

S. Liu, M. A. Rahman, C. Y. Wong, C. F. Lin, H. Wu, and N. Kwok, “Image dehazing from the perspective of noise filtering,” Computers & Electrical Engineering, in press (2016).
[Crossref]

Wu, H.

S. Liu, M. A. Rahman, C. Y. Wong, C. F. Lin, H. Wu, and N. Kwok, “Image dehazing from the perspective of noise filtering,” Computers & Electrical Engineering, in press (2016).
[Crossref]

Yang, J.

K. Tang, J. Yang, and J. Wang, “Investigating haze-relevant features in a learning framework for image dehazing,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2014).

Yang, M. H.

W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M. H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in European Conference on Computer Vision (Springer, 2016), pp. 154–169.

Yemelyanov, K. M.

Zhang, H.

W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M. H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in European Conference on Computer Vision (Springer, 2016), pp. 154–169.

Zhang, W.

Zhu, Q.

Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Transactions on Image Processing 24, 3522–3533 (2015).
[Crossref] [PubMed]

ACM Trans. Graph. (1)

J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: Model-based photograph enhancement and viewing,” ACM Trans. Graph. 27(5), 1–10 (2008).
[Crossref]

ACM Trans.Graph. (1)

R. Fattal, “Single image dehazing,” ACM Trans.Graph. 27(3), 988–992 (2008).

Appl. Opt. (3)

Computers & Electrical Engineering (1)

W. Sun, H. Wang, C. Sun, B. Guo, W. Jia, and M. Sun, “Fast single image haze removal via local atmospheric light veil estimation,” Computers & Electrical Engineering 46, 371–383 (2015).
[Crossref]

IEEE Trans. Image Process. (1)

J. P. Oakley and B. L. Satherley, “Improving image quality in poor visibility conditions using a physical model for contrast degradation,” IEEE Trans. Image Process. 7(2), 167–179 (1998).
[Crossref]

IEEE Trans. on Image Process (1)

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

IEEE Trans. on Image Processing (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Trans. on Image Processing 16, 2080–2095 (2007).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

K.M. He, J. Sun, and X.O. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010).
[PubMed]

IEEE Transactions on Image Processing (3)

C. O. Ancuti and C. Ancuti, “Single image dehazing by multi-scale fusion,” IEEE Transactions on Image Processing 22, 3271–3282 (2013).
[Crossref] [PubMed]

H. R. Sheikh, A. C. Bovik, and L. Cormack, “No-reference quality assessment using natural scene statistics: Jpeg2000,” IEEE Transactions on Image Processing 14, 1918–1927 (2005).
[Crossref] [PubMed]

Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Transactions on Image Processing 24, 3522–3533 (2015).
[Crossref] [PubMed]

Image Analysis & Stereology (1)

N. Hautière, J. P. Tarel, D. Aubert, and E. Dumont, “Blind contrast enhancement assessment by gradient ratioing at visible edges,” Image Analysis & Stereology 27, 87–95 (2011).
[Crossref]

J. Opt. Soc. Am. A (2)

Opt. Express (4)

Proc. SPIE (1)

H. Chen and L. B. Wolff, “Polarization phase-based method for material classification and object recognition in computer vision,” Proc. SPIE 2599, 54–63 (1996).
[Crossref]

Other (17)

M. Ben-Ezra, “Segmentation with invisible keying signal,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), pp. 32–37.

N. Hautière, J.P. Tarel, and D. Aubert, “Towards fog-free in-vehicle vision systems through contrast restoration,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

M. Saito, Y. Sato, K. Ikeuchi, and H. Kashiwagi, “Measurement of surface orientations of transparent objects by use of polarization in highlight,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1999), pp. 381–386.

S. G. Narasimhan and S. K. Nayar, “Interactive (de) weathering of an image using physical models,” Proceedings of IEEE Workshop Color and Photometric Methods in Computer Vision (2003).

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

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 (Kyoto, Japan, 2009), pp. 2201–2208.

D. Berman, T. treibitz, and S. Avidan, “Non-local image dehazing,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016).

K. Tang, J. Yang, and J. Wang, “Investigating haze-relevant features in a learning framework for image dehazing,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2014).

W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M. H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in European Conference on Computer Vision (Springer, 2016), pp. 154–169.

L. B. Wolff, “Using polarization to separate reflection components,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1989), pp. 363–369.
[Crossref]

L. Kratz and K. Nishino, “Factorizing scene albedo and depth from a single foggy image,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), pp. 1701–1708.

M. Bass, Devices, Measurements, and Properties, Vol. 2 of Handbook of Optics (McGraw-Hill, 1995), Chap. 22.

S. Liu, M. A. Rahman, C. Y. Wong, C. F. Lin, H. Wu, and N. Kwok, “Image dehazing from the perspective of noise filtering,” Computers & Electrical Engineering, in press (2016).
[Crossref]

Q. Yufu and Z. Zhaofan, “Matlab code for non-sky dehazing,”(MediaFire, 2017). http://www.mediafire.com/file/1q447szg0u5zvja/Dehaze_Code.zip .

A. K. Moorthy and A. C. Bovik, “A two-step framework for constructing blind image quality indices,” in Proceedings of IEEE Conference on Signal Processing Letters (IEEE, 2010) pp. 513–516.
[Crossref]

A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” in Proceedings of IEEE Conference on Signal Processing Letters (IEEE, 2013), pp. 209–212.
[Crossref]

N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, “Color image database tid2013: Peculiarities and preliminary results,” In Proc. of European Workshop on Visual Information Processing, pp. 106–111, Paris, France (2013).

Supplementary Material (1)

NameDescription
» Code 1       It is our dehazing code based on MATLAB.

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Figures (12)

Fig. 1
Fig. 1 Flowchart of proposed dehazing process.
Fig. 2
Fig. 2 Estimation of polarization difference image. (a) 0° image, (b) 60° image, (c) 120° image, (d) minimum-intensity image, (e) maximum-intensity image, and (f) polarization difference image.
Fig. 3
Fig. 3 High-brightness object interfering with estimation of A. (a) Hazy image with sky area (R channel). (b) Difference image (R channel) (Red-circled area indicates high-brightness building area and blue-circled area indicates suitable valuation area. The red-dotted region is used to indicate that the interference area has been removed in (b)).
Fig. 4
Fig. 4 Estimation of A in non-sky image. (a) Hazy images without sky area (R channel). (b) Corresponding difference images (R channel) (Red-circled area indicates high-brightness building area, while the blue-circled one indicates the appropriate valuation area. The red-dotted region is used to indicate that the interference area has been removed in (b)).
Fig. 5
Fig. 5 Schechner’s initial polarization image and processed image without sky area. (a) Image with sky area. (b) Image without sky area.
Fig. 6
Fig. 6 Dehazed images with and without sky area. (a) Dehazed image with sky area. (b) Dehazed image without sky area.
Fig. 7
Fig. 7 Denoising transmittance t. (a) Transmittance image with noise. (b) Transmittance image denoised by guided filter. (c) Transmittance image denoised by BM3D filter.
Fig. 8
Fig. 8 Effect of adding factor ε along with block-matching and 3D (BM3D) filtering. (a) Dehazed image with noise. (b) RGB-channel image with noise. (c) RGB-channel image processed using factor ε. (d) Color image processed using factor ε. (e) RGB-channel image processed by BM3D. (f) Color image processed by BM3D.
Fig. 9
Fig. 9 Filtering of amplified noise. (a) Maximum-intensity image. (b) Minimum-intensity image. (c) Dehazed image with noise. (d) Dehazed image processed by factor ε. (e) Preliminary noise image. (f) Three-channel weight map. (g) New noise image processed using weight map. (h) Final denoised image. (i) Local noise effect in Fig. 9(h). (j) Schechner’s result. (k) Local noise effect in Fig. 9(j).
Fig. 10
Fig. 10 Comparison of algorithm performance using Schechner’s dataset.
Fig. 11
Fig. 11 Comparison of algorithm performance using our dataset.
Fig. 12
Fig. 12 Algorithm performance in the scene with specular surface.

Tables (2)

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Table 1 Quantitative comparison of state-of-art algorithms for various performance indices.

Tables Icon

Table 2 Quantitative comparison of state-of-art algorithms for various performance indices.

Equations (21)

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

I = L object t + A = L object e - β d + A ( 1 - e - β d )
L object = I + I - Δ I / p 1 - Δ I p A = I - A 1 - A A
[ I out Q out U out V out ] = M [ I in Q in U in V in ] = 1 2 [ I in + Q in cos 2 θ + U in sin 2 θ I in cos 2 θ + Q in cos 2 2 θ + U in cos 2 θ sin 2 θ I in sin 2 θ + Q in cos 2 θ sin 2 θ + U in sin 2 2 θ 0 ]
I out = I in + Q in cos 2 θ + U in sin 2 θ
I = A + D
A = A ( 1 - e - β d )
D = L object e - β d
p = A - A A + A = A - A A
I = D 2 + A
I = D 2 + A
p = A - A A = I - I A
Δ I = I - I = A p
Cov ( A , L object ) = 0
Cov ( Δ I p , p I - Δ I p A - Δ I ) = 0
argmin | Cov ( Δ I p , p I - Δ I p A - Δ I ) |
[ p r p g p b ] [ 0.29 0.27 0.24 ]
[ p r p g p b ] [ 0.34 0.32 0.27 ]
N = L object - L ε object
Δ I = I - I = A p + D p D
A ^ = I - I p = A + D p D p
D ^ = I - A ^ = D ( 1 - p D p )

Metrics