Abstract
The majority of the existing methods for image dehazing are of more complexity, which exhibits more time for execution. Therefore, these algorithms may not be suitable for real-time image processing systems. Also, the existing methods do not consider the local variations of the hazy image that may result in over-saturation and under-saturation. Hence, there is a requirement to design a fast dehazing algorithm that adaptively dehazes according to the local region characteristics. In the proposed technique, a hazy image is first classified into ‘less-affected by haze’ and ‘more-affected by haze’ regions, on the basis of pixel intensity values. The image decomposition, image dehazing, and details enhancement are implemented separately in two blocks, namely ‘less-affected by haze’ and ‘more-affected by haze’ blocks, with different scale factors for adaptive dehazing. The results of these two blocks are fused based upon the regional categorization. The proposed algorithm produces good dehazed results at the rate of 25 frames per second.
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References
Huang SC, Chen BH, Cheng YJ (2014) An efficient visibility enhancement algorithm for road scenes captured by intelligent transportation systems. IEEE Trans Intell Transp Syst 15(5):2321–2332
Wang W, Yuan X (2017) Recent advances in image dehazing. IEEE CAA J. Autom. Sinica 4(3):410–436
Kim JH, Jang WD, Sim JY, Kim CS (2013) Optimized contrast enhancement for real-time image and video dehazing. J Vis Commun Image Represent 24(3):410–425
Li Z, Tan P, Tan RT, Zou D, Zhiying Zhou S, Cheong LF (2015) Simultaneous video defogging and stereo reconstruction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4988–4997
Ancuti CO, Ancuti C (2013) Single image dehazing by multi-scale fusion. IEEE Trans Image Process 22(8):3271–3282
Choi LK, You J, Bovik AC (2015) Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process 24(11):3888–3901
He K, Sun J, Tang X (2010) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353
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
Zhao D, Xu L, Yan Y, Chen J, Duan LY (2019) Multi-scale optimal fusion model for single image dehazing. Signal Process Image Commun 74:253–265
Ngo D, Lee S, Kang B (2020) Robust single-image haze removal using optimal transmission map and adaptive atmospheric light. Remote Sens 12(14):2233
Cai B, Xu X, Jia K, Qing C, Tao D (2016) Dehazenet: an end-to-end system for single image haze removal. IEEE Trans Image Process 25(11):5187–5198
Haouassi S, Wu D (2020) Image dehazing based on (CMTnet) cascaded multi-scale convolutional neural networks and efficient light estimation algorithm. Appl Sci 10(3):1190
Kumar BP, Kumar A, Pandey R (2022) Region-based adaptive single image dehazing, detail enhancement and pre-processing using auto-colour transfer method. Signal Process Image Commun 100:116532
Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2018) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28(1):492–505
He K, Sun J (2015) Fast guided filter. arXiv:1505.00996
Talebi H, Milanfar P (2016) Fast multilayer Laplacian enhancement. IEEE Trans Comput Imaging 2(4):496–509
Koschmieder H (1924) Theorie der horizontalen Sichtweite. Beitrage zur Physik der freien Atmosphare, 33–53
Cai B, Xu X, Tao D (2016) Real-time video dehazing based on spatio-temporal mrf. In: Pacific Rim conference on multimedia. Springer, Cham, pp 315–325
Shin YS, Cho Y, Pandey G, Kim A (2016) Estimation of ambient light and transmission map with common convolutional architecture. In: OCEANS 2016, MTS/IEEE. Monterey. IEEE, pp 1–7
Sakaridis C, Dai D, Van Gool L (2018) Semantic foggy scene understanding with synthetic data. Int J Comput Vis 126(9):973–992
Sakaridis C, Dai D, Hecker S, Van Gool L (2018) Model adaptation with synthetic and real data for semantic dense foggy scene understanding. In: Proceedings of the European conference on computer vision (ECCV), pp 687–704
Mittal A, Soundararajan R, Bovik AC (2012) Making a “completely blind” image quality analyzer. IEEE Signal Process Lett 20(3):209–212
Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–4708
Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801–818
Tarel JP, Hautiere N, Cord A, Gruyer D, Halmaoui H (2010) Improved visibility of road scene images under heterogeneous fog. In: 2010 IEEE intelligent vehicles symposium, pp 478–485
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Kumar, B.P., Kumar, A., Pandey, R. (2023). Fast Adaptive Image Dehazing and Details Enhancement of Hazy Images. In: Yadav, R.P., Nanda, S.J., Rana, P.S., Lim, MH. (eds) Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-8742-7_18
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DOI: https://doi.org/10.1007/978-981-19-8742-7_18
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