Abstract
Haze and fog had a great influence on the quality of images, and to eliminate this, dehazing and defogging are applied. For this purpose, an effective and automatic dehazing method is proposed. To dehaze a hazy image, we need to estimate two important parameters such as atmospheric light and transmission map. For atmospheric light estimation, the superpixels segmentation method is used to segment the input image. Then each superpixel intensities are summed and further compared with each superpixel individually to extract the maximum intense superpixel. Extracting the maximum intense superpixel from the outdoor hazy image automatically selects the hazy region (atmospheric light). Thus, we considered the individual channel intensities of the extracted maximum intense superpixel as an atmospheric light for our proposed algorithm. Secondly, on the basis of measured atmospheric light, an initial transmission map is estimated. The transmission map is further refined through a rolling guidance filter that preserves much of the image information such as textures, structures and edges in the final dehazed output. Finally, the haze-free image is produced by integrating the atmospheric light and refined transmission with the haze imaging model. Through detailed experimentation on several publicly available datasets, we showed that the proposed model achieved higher accuracy and can restore high-quality dehazed images as compared to the state-of-the-art models. The proposed model could be deployed as a real-time application for real-time image processing, real-time remote sensing images, real-time underwater images enhancement, video-guided transportation, outdoor surveillance, and auto-driver backed systems.
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Koschmieder, H.: Theorie der horizontalen sichtweite. Beitrage zur Physik der freien Atmosphare, pp. 33–53 (1924)
Shehata, M.S., Cai, J., Badawy, W.M., Burr, T.W., Pervez, M.S., Johannesson, R.J., Radmanesh, A.: Video-based automatic incident detection for smart roads: the outdoor environmental challenges regarding false alarms. IEEE Trans. Intell. Transp. Syst. 9(2), 349–360 (2008)
Bronte, S., Bergasa, L. M., Alcantarilla, P. F.: Fog detection system based on computer vision techniques. In: 2009 12th International IEEE Conference on Intelligent Transportation Systems, pp. 1–6, IEEE
Huang, S.-C., Chen, B.-H., Cheng, Y.-J.: An efficient visibility enhancement algorithm for road scenes captured by intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 15(5), 2321–2332 (2014)
Huang, S.-C.: An advanced motion detection algorithm with video quality analysis for video surveillance systems. IEEE Trans. Circuits Syst. Video Technol. 21(1), 1–14 (2010)
Jia, Z., Wang, H., Caballero, R.E., Xiong, Z., Zhao, J., Finn, A.: A two-step approach to see-through bad weather for surveillance video quality enhancement. Mach. Vis. Appl. 23(6), 1059–1082 (2012)
Wang, W., Yuan, X.: Recent advances in image dehazing. IEEE/CAA J. Autom. Sin. 4(3), 410–436 (2017)
Jiang, H., Lu, N.: Multi-scale residual convolutional neural network for haze removal of remote sensing images. Remote Sens. 10(6), 945 (2018)
Shen, Y., Wang, Y., Lv, H., Qian, J.: Removal of thin clouds in landsat-8 oli data with independent component analysis. Remote Sens. 7(9), 11481–11500 (2015)
Sun, L., Latifovic, R., Pouliot, D.: Haze removal based on a fully automated and improved haze optimized transformation for landsat imagery over land. Remote Sens. 9(10), 972 (2017)
Ahmad, M., Khan, A. M., Hussain, R., Protasov, S., Chow, F., Khattak, A. M.: Unsupervised geometrical feature learning from hyperspectral data. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–6 (2016)
Pavlic, M., Rigoll, G., Ilic, S.: Classification of images in fog and fog-free scenes for use in vehicles. In: 2013 IEEE Intelligent Vehicles Symposium (IV), pp. 481–486, IEEE
Spinneker, R., Koch, C., Park, S.-B., Yoon, J. J.: Fast fog detection for camera based advanced driver assistance systems. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1369–1374, IEEE
Negru, M., Nedevschi, S., Peter, R.I.: Exponential contrast restoration in fog conditions for driving assistance. IEEE Trans. Intell. Transp. Syst. 16(4), 2257–2268 (2015)
Kim, T.K., Paik, J.K., Kang, B.S.: Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans. Consum. Electron. 44(1), 82–87 (1998)
Dippel, S., Stahl, M., Wiemker, R., Blaffert, T.: Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform. IEEE Trans. Med. Imaging 21(4), 343–353 (2002)
Cooper, T.J., Baqai, F.A.: Analysis and extensions of the frankle-mccann retinex algorithm. J. Electron. Imaging 13(1), 85–93 (2004)
Seow, M.-J., Asari, V.K.: Ratio rule and homomorphic filter for enhancement of digital colour image. Neurocomputing 69(7–9), 954–958 (2006)
Hautiere, N., Tarel, J.-P., Aubert, D.: Towards fog-free in-vehicle vision systems through contrast restoration. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, Citeseer (2007)
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, vol. 27. ACM (2008)
Narasimhan, S. G., Nayar, S. K.: Interactive (de) weathering of an image using physical models. In: IEEE Workshop on color and photometric Methods in computer Vision, vol. 6, p. 1, France
Nayar, S. K., Narasimhan, S. G.: Vision in bad weather. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 820–827, IEEE
Schechner, Y. Y., Narasimhan, S. G., Nayar, S. K.: Instant dehazing of images using polarization. In: CVPR (1), pp. 325–332
Tian, Y., Xiao, C., Chen, X., Yang, D., Chen, Z.: Haze removal of single remote sensing image by combining dark channel prior with superpixel. Electron. Imaging 2016(2), 1–6 (2016)
Oakley, J.P., Satherley, B.L.: Improving image quality in poor visibility conditions using a physical model for contrast degradation. IEEE Trans. Image Process. 7(2), 167–179 (1998)
Ancuti, C.O., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Trans. Image Process. 22(8), 3271–3282 (2013)
Kratz, L., Nishino, K.: Factorizing scene albedo and depth from a single foggy image. In: IEEE 12th International Conference on Computer Vision, pp. 1701–1708, IEEE (2009)
Wang, Z., Feng, Y.: Fast single haze image enhancement. Comput. Electr. Eng. 40(3), 785–795 (2014)
Shwartz, S., Namer, E., Schechner, Y. Y.: Blind haze separation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1984–1991, IEEE (2006)
Tavallali, P., Yazdi, M., Khosravi, M.R.: Robust cascaded skin detector based on adaboost. Multimed. Tools Appl. 78(2), 2599–2620 (2019)
Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 713–724 (2003)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010)
Tan, R. T.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, IEEE (2008)
Tripathi, A., Mukhopadhyay, S.: Single image fog removal using anisotropic diffusion. IET Image Process. 6(7), 966–975 (2012)
Fattal, R.: Single image dehazing. ACM Trans. Graph. 27(3), 72 (2008)
Tarel, J.-P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: IEEE 12th International Conference on Computer Vision, pp. 2201–2208, IEEE (2009)
Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24(11), 3522–3533 (2015)
Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. 34(1), 13 (2014)
Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 617–624
Kim, J.-H., Jang, W.-D., Sim, J.-Y., Kim, C.-S.: Optimized contrast enhancement for real-time image and video dehazing. J. Vis. Commun. Image Represent. 24(3), 410–425 (2013)
Gibson, K. B., Nguyen, T. Q.: Fast single image fog removal using the adaptive wiener filter. In: IEEE International Conference on Image Processing, pp. 714–718, IEEE (2013)
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2012)
McCartney, E.J.: Optics of the Atmosphere: Scattering by Molecules and Particles, p. 421. Wiley, New York (1976)
Preetham, A.J., Shirley, P., Smits, B.: A practical analytic model for daylight. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 91–100 (1999)
Abbasi, R., Luo, B., Rehman, G., Hassan, H., Iqbal, M.S., Xu, L.: A new multilevel reversible bit-planes data hiding technique based on histogram shifting of efficient compressed domain. Vietnam J. Comput. Sci. 5(2), 185–196 (2018)
Abbasi, R., et al.: Efficient lossless compression based reversible data hiding using multilayered n-bit localization. Secur. Commun. Netw. 2019, 8981240 (2019)
Hassan, H., Bashir, A.K., Abbasi, R., Ahmad, W., Luo, B.: Single image defocus estimation by modified Gaussian function. Trans. Emerg. Telecommun. Technol. 30(6), e3611 (2019)
Wang, W., Chang, F., Ji, T., Wu, X.: A fast single-image dehazing method based on a physical model and gray projection. IEEE Access 6, 5641–5653 (2018)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Zhang, Q., Shen, X., Xu, L., Jia, J.: Rolling guidance filter. In: European conference on computer vision, pp. 815–830, Springer
Lindeberg, T.: Scale-space theory: a basic tool for analyzing structures at different scales. J. Appl. Stat. 21(1–2), 225–270 (1994)
Ancuti, C. O., Ancuti, C., Timofte, R., De Vleeschouwer, C.: O-haze: a dehazing benchmark with real hazy and haze-free outdoor images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 754–762
Berman, D., Avidan, S.: Non-local image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1674–1682
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(11), 5187–5198 (2016)
Al Sharif, S., Al Ali, M., Al Reqabi, N., Iqbal, F., Baker, T., Marrington, A.: Magec: an image searching tool for detecting forged images in forensic investigation. In: 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS), pp. 1–6, IEEE (2016)
Ahmad, M., Bashir, A.K., Khan, A.M.: Metric similarity regularizer to enhance pixel similarity performance for hyperspectral unmixing. Optik 140, 86–95 (2017)
Singh, D., Kumar, V.: Dehazing of remote sensing images using improved restoration model based dark channel prior. Imaging Sci. J. 65(5), 282–292 (2017)
Tahir, Z., Qureshi, A.H., Ayaz, Y., Nawaz, R.: Potentially guided bidirectionalized RRT* for fast optimal path planning in cluttered environments. Robot. Auton. Syst. 108, 13–27 (2018)
Ju, M., Ding, C., Guo, Y.J., Zhang, D.: Remote sensing image haze removal using gamma-correction-based dehazing model. IEEE Access 7, 5250–5261 (2018)
Abdulhussain, S. H., Ramli, A. R., Mahmmod, B. M., Saripan, M. I., Al-Haddad, S., Baker, T., Flayyih, W. N., Jassim, W. A.: A fast feature extraction algorithm for image and video processing. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, IEEE (2019)
Jain, D.K., et al.: An efficient and adaptable multimedia system for converting PAL to VGA in real-time video processing. J. Real-Time Image Process. (2019). https://doi.org/10.1007/s11554-019-00889-4
Ayyappan, S., Lakshmi, C., Menon, V.: A secure reversible data hiding and encryption system for embedding EPR in medical images. J. Curr. Signal Transduct. Ther. 14, 1 (2019). https://doi.org/10.2174/1574362414666190304162411
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Hassan, H., Bashir, A.K., Ahmad, M. et al. Real-time image dehazing by superpixels segmentation and guidance filter. J Real-Time Image Proc 18, 1555–1575 (2021). https://doi.org/10.1007/s11554-020-00953-4
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DOI: https://doi.org/10.1007/s11554-020-00953-4