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Variational histogram equalization for single color image defogging. (English) Zbl 1400.94044
Summary: Foggy images taken in the bad weather inevitably suffer from contrast loss and color distortion. Existing defogging methods merely resort to digging out an accurate scene transmission in ignorance of their unpleasing distortion and high complexity. Different from previous works, we propose a simple but powerful method based on histogram equalization and the physical degradation model. By revising two constraints in a variational histogram equalization framework, the intensity component of a fog-free image can be estimated in HSI color space, since the airlight is inferred through a color attenuation prior in advance. To cut down the time consumption, a general variation filter is proposed to obtain a numerical solution from the revised framework. After getting the estimated intensity component, it is easy to infer the saturation component from the physical degradation model in saturation channel. Accordingly, the fog-free image can be restored with the estimated intensity and saturation components. In the end, the proposed method is tested on several foggy images and assessed by two no-reference indexes. Experimental results reveal that our method is relatively superior to three groups of relevant and state-of-the-art defogging methods.
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
68U10 Computing methodologies for image processing
Full Text: DOI
[1] Hautière, N.; Tarel, J.-P.; Halmaoui, H.; Brémond, R.; Aubert, D., Enhanced fog detection and free-space segmentation for car navigation, Machine Vision and Applications, 25, 3, 667-679, (2014)
[2] Liu, H.; Yang, J.; Wu, Z.; Zhang, Q., Fast single image dehazing based on image fusion, Journal of Electronic Imaging, 24, 1, (2015)
[3] Wang, J.-G.; Tai, S.-C.; Lin, C.-J., Image haze removal using a hybrid of fuzzy inference system and weighted estimation, Journal of Electronic Imaging, 24, 3, 1-14, (2015)
[4] Narasimhan, N. S.; Nayar, S., Interactive (de) weathering of an image using physical models, Proceedings of the IEEE Workshop on Color and Photometric Methods in Computer Vision
[5] Schechner, Y. Y.; Narasimhan, S. G.; Nayar, S. K., Instant dehazing of images using polarization, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
[6] Narasimhan, S. G.; Nayar, S. K., Vision and the atmosphere, International Journal of Computer Vision, 48, 3, 233-254, (2002) · Zbl 1012.68760
[7] Lee, Y.; Gibson, K. B.; Lee, Z.; Nguyen, T. Q., Stereo image defogging, Proceedings of the IEEE International Conference on Image Processing (ICIP ’14)
[8] Kopf, J.; Neubert, B.; Chen, B.; Cohen, M.; Cohen-Or, D.; Deussen, O.; Uyttendaele, M.; Lischinski, D., Deep photo: Model-based photograph enhancement and viewing, ACM Transactions on Graphics, 27, 5, article 116, (2008)
[9] He, K.-M.; Sun, J.; Tang, X.-O., Single image haze removal using dark channel prior, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 12, 2341-2353, (2010)
[10] Nishino, K.; Kratz, L.; Lombardi, S., Bayesian defogging, International Journal of Computer Vision, 98, 3, 263-278, (2012)
[11] Arigela, S.; Asari, V. K.; Bebis, G.; Boyle, R.; Parvin, B., Enhancement of hazy color images using a self-tunable transformation function, Advances in Visual Computing. Advances in Visual Computing, Lecture Notes in Computer Science, 8888, 578-587, (2014), New York, NY, USA: Springer, New York, NY, USA
[12] Liu, Q.; Chen, M. Y.; Zhou, D. H., Single image haze removal via depth-based contrast stretching transform, Science China Information Sciences, 58, 1, 1-17, (2015)
[13] Ranota, H. K.; Kaur, P., A new single image dehazing approach using modified dark channel prior, Advances in Intelligent Systems and Computing, 320, 77-85, (2015)
[14] Wang, W.; Ng, M. K., A variational histogram equalization method for image contrast enhancement, SIAM Journal on Imaging Sciences, 6, 3, 1823-1849, (2013) · Zbl 1281.65044
[15] Gonzalez, R. C.; Woods, R. E., Digital Image Processing, (2010), Beijing, China: Publishing House of Electronics Industry, Beijing, China
[16] Jafar, I.; Ying, H., Image contrast enhancement by constrained variational histogram equalization, Proceedings of the IEEE International Conference on Electro/Information Technology (EIT ’07)
[17] Fattal, R., Single image dehazing, ACM Transactions on Graphics, 27, 3, article 72, (2008)
[18] Tan, R. T., Visibility in bad weather from a single image, Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’08)
[19] Rudin, L. I.; Osher, S.; Fatemi, E., Nonlinear total variation based noise removal algorithms, Physica D. Nonlinear Phenomena, 60, 1–4, 259-268, (1992) · Zbl 0780.49028
[20] Aubert, G.; Kornprobst, P., Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, 147, (2002), New York, NY, USA: Springer, New York, NY, USA · Zbl 1109.35002
[21] Lin, F.; Fardad, M.; Jovanovic, M. R., Design of optimal sparse feedback gains via the alternating direction method of multipliers, IEEE Transactions on Automatic Control, 58, 9, 2426-2431, (2013) · Zbl 1369.93215
[22] Vogel, C. R.; Oman, M. E., Fast, robust total variation-based reconstruction of noisy, blurred images, IEEE Transactions on Image Processing, 7, 6, 813-824, (1998) · Zbl 0993.94519
[23] Marquina, A.; Osher, S., Explicit algorithms for a new time dependent model based on level set motion for nonlinear deblurring and noise removal, SIAM Journal on Scientific Computing, 22, 2, 387-405, (2000) · Zbl 0969.65081
[24] Chan, T. F.; Osher, S.; Shen, J., The digital TV filter and nonlinear denoising, IEEE Transactions on Image Processing, 10, 2, 231-241, (2001) · Zbl 1039.68778
[25] Aubert, G.; Vese, L., A variational method in image recovery, SIAM Journal on Numerical Analysis, 34, 5, 1948-1979, (1997) · Zbl 0890.35033
[26] Kim, J.-H.; Sim, J.-Y.; Kim, C.-S., Single image dehazing based on contrast enhancement, Proceedings of the 36th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ’11)
[27] Sulami, M.; Glatzer, I.; Fattal, R.; Werman, M., Automatic recovery of the atmospheric light in hazy images, Proceedings of the 6th IEEE International Conference on Computational Photography (ICCP ’14)
[28] Zhu, Q.; Mai, J.; Shao, L., A fast single image haze removal algorithm using color attenuation prior, IEEE Transactions on Image Processing, 24, 11, 3522-3533, (2015)
[29] Yeh, C.-H.; Kang, L.-W.; Lin, C.-Y.; Lin, C.-Y., Efficient image/video dehazing through haze density analysis based on pixel-based dark channel prior, Proceedings of the 3rd International Conference on Information Security and Intelligent Control (ISIC ’12)
[30] Tang, K.; Yang, J.; Wang, J., Investigating haze-relevant features in a learning framework for image dehazing, Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’14)
[31] Mittal, A.; Moorthy, A. K.; Bovik, A., No-reference image quality assessment in the spatial domain, IEEE Transactions on Image Processing, 21, 12, 4695-4708, (2012) · Zbl 1373.94286
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