Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations.

Authors

DOI:

https://doi.org/10.9781/ijimai.2021.11.009

Keywords:

Image Dehazing, Image Defogging, Image Quality Assessment

Abstract

Our study aims to review and analyze the most relevant studies in the image dehazing field. Many aspects have been deemed necessary to provide a broad understanding of various studies that have been examined through surveying the existing literature. These aspects are as follows: datasets that have been used in the literature, challenges that other researchers have faced, motivations, and recommendations for diminishing the obstacles in the reported literature. A systematic protocol is employed to search all relevant articles on image dehazing, with variations in keywords, in addition to searching for evaluation and benchmark studies. The search process is established on three online databases, namely, IEEE Xplore, Web of Science (WOS), and ScienceDirect (SD), from 2008 to 2021. These indices are selected because they are sufficient in terms of coverage. Along with definition of the inclusion and exclusion criteria, we include 152 articles to the final set. A total of 55 out of 152 articles focused on various studies that conducted image dehazing, and 13 out 152 studies covered most of the review papers based on scenarios and general overviews. Finally, most of the included articles centered on the development of image dehazing algorithms based on real-time scenario (84/152) articles. Image dehazing removes unwanted visual effects and is often considered an image enhancement technique, which requires a fully automated algorithm to work under real-time outdoor applications, a reliable evaluation method, and datasets based on different weather conditions. Many relevant studies have been conducted to meet these critical requirements. We conducted objective image quality assessment experimental comparison of various image dehazing algorithms. In conclusions unlike other review papers, our study distinctly reflects different observations on image dehazing areas. We believe that the result of this study can serve as a useful guideline for practitioners who are looking for a comprehensive view on image dehazing.

Downloads

Download data is not yet available.

References

S. D. Roy and M. K. Bhowmik, “A survey on visibility enhancement techniques in degraded atmospheric outdoor scenes,” in 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 2017, pp. 349-352.

S. D. Roy, M. K. Bhowmik, and S. S. Saha, “Qualitative evaluation of visibility enhancement techniques on SAMEER-TU database for security and surveillance,” in 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2017, pp. 1-7.

S. G. Narasimhan and S. K. Nayar, “Contrast restoration of weather degraded images,” IEEE transactions on pattern analysis and machine intelligence, vol. 25, no. 6, pp. 713-724, 2003.

Q. Zhu, J. Mai, and L. Shao, “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior,” IEEE Trans. Image Processing, vol. 24, no. 11, pp. 3522-3533, 2015.

D. Nair and P. Sankaran, “Color image dehazing using surround filter and dark channel prior,” Journal of Visual Communication and Image Representation, vol. 50, pp. 9-15, 2018.

Y. Xu, J. Wen, L. Fei, and Z. Zhang, “Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement,” IEEE Access, vol. 4, pp. 165-188, 2016.

S. G. Narasimhan and S. K. Nayar, “Vision and the atmosphere,” International Journal of Computer Vision, vol. 48, no. 3, pp. 233-254, 2002.

W. Wang and X. Yuan, “Recent advances in image dehazing,” IEEE/CAA Journal of Automatica Sinica, vol. 4, no. 3, pp. 410-436, 2017.

A. K. Tripathi and S. Mukhopadhyay, “Single image fog removal using anisotropic diffusion,” IET Image Processing, vol. 6, no. 7, pp. 966-975, 2012.

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 12, pp. 2341-2353, 2011.

R. Fattal, “Dehazing using color-lines,” ACM transactions on graphics (TOG), vol. 34, no. 1, p. 13, 2014.

Z. Lin and X. Wang, “Dehazing for image and video using guided filter,” Open J. Appl. Sci, vol. 2, no. 4B, pp. 123-127, 2012.

G. Yadav, S. Maheshwari, and A. Agarwal, “Fog removal techniques from images: A comparative review and future directions,” in 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014), 2014, pp. 44-52.

K. B. Gibson, D. T. Vo, and T. Q. Nguyen, “An investigation of dehazing effects on image and video coding,” IEEE transactions on image processing, vol. 21, no. 2, pp. 662-673, 2012.

S.-C. Huang, B.-H. Chen, and W.-J. Wang, “Visibility restoration of single hazy images captured in real-world weather conditions,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 10, pp. 1814-1824, 2014.

M. Wang and S.-d. Zhou, “The study of color image defogging based on wavelet transform and single scale retinex,” in International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, 2011, vol. 8194, p. 81940F: International Society for Optics and Photonics.

X. Zhao, R. Wang, and Y. Qiu, “An enhancement method of fog-degraded images,” in Second International Conference on Digital Image Processing, 2010, vol. 7546, p. 75461S: International Society for Optics and Photonics.

M.-Z. Zhu, B.-W. He, and L.-W. Zhang, “Atmospheric light estimation in hazy images based on color-plane model,” Computer Vision and Image Understanding, vol. 165, pp. 33-42, 2017.

P. Bekaert, T. Haber, C. Ancuti, and C. Ancuti, “Enhancing underwater images and videos by fusion,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 81-88: IEEE.

X. Fu, Y. Huang, D. Zeng, X.-P. Zhang, and X. Ding, “A fusion-based enhancing approach for single sandstorm image,” in Multimedia Signal Processing (MMSP), 2014 IEEE 16th International Workshop on, 2014, pp. 1-5: IEEE.

R. Fattal, “Single image dehazing,” ACM transactions on graphics (TOG), vol. 27, no. 3, p. 72, 2008.

Q. Zhu, Z. Hu, and K. Ivanov, “Quantitative assessment mechanism transcending visual perceptual evaluation for image dehazing,” in 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2015, pp. 808-813.

Z. Y. Hu and Q. Liu, “A Method for Dehazed Image Quality Assessment,” in Practical Applications of Intelligent Systems, Iske 2013, vol. 279, Z. Wen and T. Li, Eds. Advances in Intelligent Systems and Computing, 2014, pp. 909-913.

J. Mai, Q. Zhu, and D. Wu, “The latest challenges and opportunities in the current single image dehazing algorithms,” in 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), 2014, pp. 118-123.

B. Li et al., “Benchmarking Single-Image Dehazing and Beyond,” IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 492-505, 2019.

T. Pal, M. K. Bhowmik, D. Bhattacharjee, and A. K. Ghosh, “Visibility enhancement techniques for fog degraded images: A comparative analysis with performance evaluation,” in 2016 IEEE Region 10 Conference (TENCON), 2016, pp. 2583-2588.

J. Kaur and P. Kaur, “Comparative study on various single image defogging techniques,” in 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2017, pp. 357-361.

K. H. Abdulkareem et al., “A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods,” Neural Computing and Applications, 2020/05/26 2020.

K. H. Abdulkareem, et al., “A Novel Multi-Perspective Benchmarking Framework for Selecting Image Dehazing Intelligent Algorithms Based on BWM and Group VIKOR Techniques,” International Journal of Information Technology & Decision Making, vol. 19, 2020.

K. Wang, H. Wang, Y. Li, Y. Hu, and Y. Li, “Quantitative Performance Evaluation for Dehazing Algorithms on Synthetic Outdoor Hazy Images,” IEEE Access, vol. 6, pp. 20481-20496, 2018.

J. Perez, P. J. Sanz, M. Bryson, and S. B. Williams, “A benchmarking study on single image dehazing techniques for underwater autonomous vehicles,” in OCEANS 2017 - Aberdeen, 2017, pp. 1-9.

Z. A. Hu, Q. S. Zhu, and Ieee, “AN EFFECTIVE PERFORMANCE RANKING MECHANISM TO IMAGE DEHAZING METHODS WITH PSYCHOLOGICAL INFERENCE BENCHMARK,” in 2016 Ieee International Conference on Acoustics, Speech and Signal Processing Proceedings (International Conference on Acoustics Speech and Signal Processing ICASSP, 2016, pp. 1576-1580.

Z. Chen, T. Jiang, and Y. Tian, “Quality Assessment for Comparing Image Enhancement Algorithms,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 3003-3010.

B. Li, M. Tian, W. Zhang, H. Yao, X. J. J. o. V. C. Wang, and I. Representation, “Learning to predict the quality of distorted-then-compressed images via a deep neural network,” vol. 76, p. 103004, 2021.

X. Liu and J. Y. Hardeberg, “Fog removal algorithms: Survey and perceptual evaluation,” in European Workshop on Visual Information Processing (EUVIP), 2013, pp. 118-123.

K. D. Ma, W. T. Liu, Z. Wang, and Ieee, “PERCEPTUAL EVALUATION OF SINGLE IMAGE DEHAZING ALGORITHMS,” in 2015 Ieee International Conference on Image Processing (IEEE International Conference on Image Processing ICIP, 2015, pp. 3600-3604.

J. El Khoury, S. Le Moan, J.-B. Thomas, A. J. M. t. Mansouri, and applications, “Color and sharpness assessment of single image dehazing,” vol. 77, no. 12, pp. 15409-15430, 2018.

C. H. Hsieh, S. C. Horng, Z. J. Huang, and Q. Zhao, “Objective Haze Removal Assessment Based on Two-Objective Optimization,” in 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), 2017, pp. 279-283.

F. Guo, J. Tang, and Z. X. Cai, “Objective measurement for image defogging algorithms,” Journal of Central South University, vol. 21, no. 1, pp. 272-286, Jan 2014.

Y. Wang et al., “An imaging-inspired no-reference underwater color image quality assessment metric,” Computers & Electrical Engineering, 2017.

S. Fang, J. R. Yang, J. Q. Zhan, H. W. Yuan, R. Z. Rao, and Ieee, “Image Quality Assessment on Image Haze Removal,” in 2011 Chinese Control and Decision Conference, pp. 610-614.

X. X. Pan, F. Y. Xie, Z. G. Jiang, Z. W. Shi, and X. Y. Luo, “No-Reference Assessment on Haze for Remote-Sensing Images,” Ieee Geoscience and Remote Sensing Letters, vol. 13, no. 12, pp. 1855-1859, Dec 2016.

K. Li, Y. Li, S. You, and N. Barnes, “Photo-Realistic Simulation of Road Scene for Data-Driven Methods in Bad Weather,” in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 491-500.

J. El Khoury, J. B. Thomas, and A. Mansouri, “A Color Image Database for Haze Model and Dehazing Methods Evaluation,” in Image and Signal Processing, vol. 9680, A. Mansouri, F. Nouboud, A. Chalifour, D. Mammass, J. Meunier, and A. ElMoataz, Eds. Lecture Notes in Computer Science, 2016, pp. 109-117.

C. Ancuti, C. O. Ancuti, and C. D. Vleeschouwer, “D-HAZY: A dataset to evaluate quantitatively dehazing algorithms,” in 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 2226-2230.

A. Duarte, F. Codevilla, J. D. O. Gaya, and S. S. C. Botelho, “A dataset to evaluate underwater image restoration methods,” in OCEANS 2016 - Shanghai, 2016, pp. 1-6.

T. Pal, M. K. Bhowmik, and A. K. Ghosh, “Defogging of Visual Images Using SAMEER-TU Database,” Procedia Computer Science, vol. 46, pp. 1676-1683, 2015.

S. H. Wang, Y. Tian, T. Pu, P. Wang, and P. Perner, “A Hazy Image Database with Analysis of the Frequency Magnitude,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 32, no. 5, May 2018, Art. no. 1854012.

Y. Li, K. Wang, N. Xu, and Y. Li, “Quantitative evaluation for dehazing algorithms on synthetic outdoor hazy dataset,” in 2017 IEEE Visual Communications and Image Processing (VCIP), 2017, pp. 1-4.

T. Zhang, H. M. Hu, and B. Li, “A Naturalness Preserved Fast Dehazing Algorithm Using HSV Color Space,” IEEE Access, vol. PP, no. 99, pp. 1-1, 2018.

H. Zhao, C. Xiao, J. Yu, and X. Xu, “Single image fog removal based on local extrema,” IEEE/CAA Journal of Automatica Sinica, vol. 2, no. 2, pp. 158-165, 2015.

S. B. Williams et al., “Monitoring of benthic reference sites: using an autonomous underwater vehicle,” IEEE Robotics & Automation Magazine, vol. 19, no. 1, pp. 73-84, 2012.

J.-P. Tarel, N. Hautiere, A. Cord, D. Gruyer, and H. Halmaoui, “Improved visibility of road scene images under heterogeneous fog,” in 2010 IEEE Intelligent Vehicles Symposium, 2010, pp. 478-485: IEEE.

J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. J. I. I. T. S. M. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” vol. 4, no. 2, pp. 6-20, 2012.

Y. Li, S. You, M. S. Brown, and R. T. Tan, “Haze visibility enhancement: A Survey and quantitative benchmarking,” Computer Vision and Image Understanding, vol. 165, pp. 1-16, 2017.

X. Zhu, Y. Li, and Y. Qiao, “Fast single image dehazing through Edge-Guided Interpolated Filter,” in 2015 14th IAPR International Conference on Machine Vision Applications (MVA), 2015, pp. 443-446.

T. M. Bui and W. Kim, “Single Image Dehazing Using Color Ellipsoid Prior,” IEEE Transactions on Image Processing, vol. 27, no. 2, pp. 999-1009, 2018.

S. Liu, M. A. Rahman, C. Y. Wong, S. C. F. Lin, G. Jiang, and N. Kwok, “Dark channel prior based image de-hazing: A review,” in 2015 5th International Conference on Information Science and Technology (ICIST), 2015, pp. 345-350.

X. Deng, H. Wang, X. Liu, and Q. Gu, “State of the art of the underwater image processing methods,” in 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2017, pp. 1-6.

M. Han, Z. Lyu, T. Qiu, and M. Xu, “A Review on Intelligence Dehazing and Color Restoration for Underwater Images,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. PP, no. 99, pp. 1-13, 2018.

G. H. Babu, N. J. J. o. V. C. Venkatram, and I. Representation, “A survey on analysis and implementation of state-of-the-art haze removal techniques,” p. 102912, 2020.

A. C. Aponso and N. Krishnarajah, “Review on state of art image enhancement and restoration methods for a vision based driver assistance system with De-weathering,” in 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2011, pp. 135-140.

C. Chengtao, Z. Qiuyu, L. Yanhua, and Ieee, “A Survey of Image Dehazing Approaches,” in 2015 27th Chinese Control and Decision Conference, 2015, pp. 3964-3969.

D. Wu, Q. Zhu, J. Wang, Y. Xie, and L. Wang, “Image haze removal: Status, challenges and prospects,” in 2014 4th IEEE International Conference on Information Science and Technology, 2014, pp. 492-497.

S. Anwar and C. J. S. P. I. C. Li, “Diving deeper into underwater image enhancement: A survey,” vol. 89, p. 115978, 2020.

W. Rong and Y. XiaoGang, “A fast method of foggy image enhancement,” in Proceedings of 2012 International Conference on Measurement, Information and Control, 2012, vol. 2, pp. 883-887.

Y. H. Shiau, P. Y. Chen, H. Y. Yang, C. H. Chen, and S. S. Wang, “Weighted haze removal method with halo prevention,” Journal of Visual Communication and Image Representation, vol. 25, no. 2, pp. 445-453, 2014.

B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “DehazeNet: An End-to-End System for Single Image Haze Removal,” IEEE Transactions on Image Processing, vol. 25, no. 11, pp. 5187-5198, 2016.

E. Zhang, K. Lv, Y. Li, and J. Duan, “A fast video image defogging algorithm based on dark channel prior,” in 2013 6th International Congress on Image and Signal Processing (CISP), 2013, vol. 01, pp. 219-223.

X. Liu, H. Zhang, Y. Y. Tang, and J. X. Du, “Scene-adaptive single image dehazing via opening dark channel model,” IET Image Processing, vol. 10, no. 11, pp. 877-884, 2016.

M. M. El-Hashash, H. A. Aly, T. A. Mahmoud, and W. Swelam, “A video haze removal system on heterogeneous cores,” in 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015, pp. 1255-1259.

M. Negru, S. Nedevschi, and R. I. Peter, “Exponential Contrast Restoration in Fog Conditions for Driving Assistance,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 4, pp. 2257-2268, 2015.

M. Negru, S. Nedevschi, and R. I. Peter, “Exponential image enhancement in daytime fog conditions,” in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014, pp. 1675-1681.

K. Roy, S. Kumar, S. Banerjee, T. S. Sarkar, and S. S. Chaudhuri, “Dehazing technique for natural scene image based on color analysis and restoration with road edge detection,” in 2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech), 2017, pp. 1-6.

F. Guo, H. Peng, and J. Tang, “Fast Defogging and Restoration Assessment Approach to Road Scene Images,” Journal of Information Science and Engineering, vol. 32, no. 3, pp. 677-702, May 2016.

N. Hautiere, J. P. Tarel, and D. Aubert, “Mitigation of Visibility Loss for Advanced Camera-Based Driver Assistance,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 2, pp. 474-484, 2010.

W. Song, B. Deng, H. Zhang, Q. Xiao, and S. Peng, “An adaptive real-time video defogging method based on context-sensitiveness,” in 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2016, pp. 406-410.

K. B. Gibson and T. Q. Nguyen, “An Analysis and Method for Contrast Enhancement Turbulence Mitigation,” IEEE Transactions on Image Processing, vol. 23, no. 7, pp. 3179-3190, 2014.

G. Yadav, S. Maheshwari, and A. Agarwal, “Contrast limited adaptive histogram equalization based enhancement for real time video system,” in 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014, pp. 2392-2397.

B. Xie, F. Guo, and Z. X. Cai, “Universal strategy for surveillance video defogging,” Optical Engineering, vol. 51, no. 10, Oct 2012, Art. no. 101703.

B. Zhang and J. Zhao, “Hardware Implementation for Real-Time Haze Removal,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 25, no. 3, pp. 1188-1192, 2017.

X. Zhao, W. Ding, C. Liu, and H. Li, “Haze removal for unmanned aerial vehicle aerial video based on spatial-temporal coherence optimisation,” IET Image Processing, vol. 12, no. 1, pp. 88-97, 2018.

W. Zhi, D. Watabe, and C. Jianting, “Improving visibility of a fast dehazing method,” in 2016 World Automation Congress (WAC), 2016, pp. 1-6.

H. Liu, D. Huang, S. Hou, and R. Yue, “Large size single image fast defogging and the real time video defogging FPGA architecture,” Neurocomputing, vol. 269, pp. 97-107, 2017.

Y. H. Shiau, Y. T. Kuo, P. Y. Chen, and F. Y. Hsu, “VLSI Design of an Efficient Flicker-Free Video Defogging Method for Real-Time Applications,” IEEE Transactions on Circuits and Systems for Video Technology, vol. PP, no. 99, pp. 1-1, 2017.

W. Wang, F. Chang, T. Ji, and X. Wu, “A Fast Single-Image Dehazing Method Based on a Physical Model and Gray Projection,” IEEE Access, vol. 6, pp. 5641-5653, 2018.

J. M. Guo, J. y. Syue, V. R. Radzicki, and H. Lee, “An Efficient Fusion-Based Defogging,” IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4217-4228, 2017.

B. Liao, P. Yin, and C. Xiao, “Efficient image dehazing using boundary conditions and local contrast,” Computers & Graphics, vol. 70, pp. 242-250, 2018.

Z. Gao and Y. Bai, “Single image haze removal algorithm using pixel-based airlight constraints,” in 2016 22nd International Conference on Automation and Computing (ICAC), 2016, pp. 267-272.

A. Kumari, H. Kodati, and S. K. Sahoo, “Fast and efficient contrast enhancement for real time video dehazing and defogging,” in 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI), 2015, pp. 1-5.

B. Xie, F. Guo, and Z. Cai, “Improved Single Image Dehazing Using Dark Channel Prior and Multi-scale Retinex,” in 2010 International Conference on Intelligent System Design and Engineering Application, 2010, vol. 1, pp. 848-851.

S. Serikawa and H. Lu, “Underwater image dehazing using joint trilateral filter,” Computers & Electrical Engineering, vol. 40, no. 1, pp. 41-50, 2014.

L. Changli, F. Tanghuai, M. Xiao, Z. Zhen, W. Hongxin, and C. Lin, “An improved image defogging method based on dark channel prior,” in 2017 2nd International Conference on Image, Vision and Computing (ICIVC), 2017, pp. 414-417.

X. Qian and L. Han, “Fast image dehazing algorithm based on multiple filters,” in 2014 10th International Conference on Natural Computation (ICNC), 2014, pp. 937-941.

W. Sun, H. Wang, C. H. Sun, B. L. Guo, W. Y. Jia, and M. G. Sun, “Fast single image haze removal via local atmospheric light veil estimation,” Computers & Electrical Engineering, vol. 46, pp. 371-383, Aug 2015.

A. Alajarmeh, R. A. Salam, K. Abdulrahim, M. F. Marhusin, A. A. Zaidan, and B. B. Zaidan, “Real-time framework for image dehazing based on linear transmission and constant-time airlight estimation,” Information Sciences, vol. 436–437, pp. 108-130, 2018.

A. Kumari and S. K. Sahoo, “Fast single image and video deweathering using look-up-table approach,” AEU - International Journal of Electronics and Communications, vol. 69, no. 12, pp. 1773-1782, 2015.

C. O. Ancuti, C. Ancuti, and P. Bekaert, “Effective single image dehazing by fusion,” in 2010 IEEE International Conference on Image Processing, 2010, pp. 3541-3544.

C. O. Ancuti and C. Ancuti, “Single Image Dehazing by Multi-Scale Fusion,” IEEE Transactions on Image Processing, vol. 22, no. 8, pp. 3271- 3282, 2013.

F. Guo, Z. Cai, B. Xie, and J. Tang, “Automatic Image Haze Removal Based on Luminance Component,” in 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), 2010, pp. 1-4.

X. Liu, F. Zeng, Z. Huang, and Y. Ji, “Single color image dehazing based on digital total variation filter with color transfer,” in 2013 IEEE International Conference on Image Processing, 2013, pp. 909-913.

J. Zhang, Y. Ding, Y. Yang, and J. Sun, “Real-time defog model based on visible and near-infrared information,” in 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2016, pp. 1-6.

I. Riaz, T. Yu, Y. Rehman, and H. Shin, “Single image dehazing via reliability guided fusion,” Journal of Visual Communication and Image Representation, vol. 40, Part A, pp. 85-97, 2016.

N. S. Pal, S. Lal, and K. Shinghal, “Visibility enhancement of images degraded by hazy weather conditions using modified non-local approach,” Optik, vol. 163, pp. 99-113, 2018.

D. Huang, K. Chen, J. Lu, and W. Wang, “Single Image Dehazing Based on Deep Neural Network,” in 2017 International Conference on Computer Network, Electronic and Automation (ICCNEA), 2017, pp. 294-299.

X. Jiang, J. Sun, H. Ding, and C. Li, “Video Image De-fogging Recognition Algorithm based on Recurrent Neural Network,” IEEE Transactions on Industrial Informatics, vol. PP, no. 99, pp. 1-1, 2018.

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, 2016, pp. 154-169: Springer.

S. Salazar-Colores, I. Cruz-Aceves, and J.-M. Ramos-Arreguin, “Single image dehazing using a multilayer perceptron,” Journal of Electronic Imaging, vol. 27, no. 4, p. 043022, 2018.

J. T. Kirk, Light and photosynthesis in aquatic ecosystems. Cambridge university press, 1994.

J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intelligent Transportation Systems Magazine, vol. 4, no. 2, pp. 6-20, 2012.

H. Lu, Y. Li, Y. Zhang, M. Chen, S. Serikawa, and H. Kim, “Underwater optical image processing: a comprehensive review,” Mobile networks and applications, vol. 22, no. 6, pp. 1204-1211, 2017.

B. H. Chen, S. C. Huang, and F. C. Cheng, “A High-Efficiency and High-Speed Gain Intervention Refinement Filter for Haze Removal,” Journal of Display Technology, vol. 12, no. 7, pp. 753-759, 2016.

J.-H. Kim, W.-D. Jang, J.-Y. Sim, and C.-S. Kim, “Optimized contrast enhancement for real-time image and video dehazing,” Journal of Visual Communication and Image Representation, vol. 24, no. 3, pp. 410-425, 2013.

C.-H. Yeh, L.-W. Kang, M.-S. Lee, and C.-Y. Lin, “Haze effect removal from image via haze density estimation in optical model,” Optics express, vol. 21, no. 22, pp. 27127-27141, 2013.

Y.-H. Shiau, H.-Y. Yang, P.-Y. Chen, and Y.-Z. Chuang, “Hardware implementation of a fast and efficient haze removal method,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 8, pp. 1369-1374, 2013.

W. Wang, X. Yuan, X. Wu, and Y. Liu, “Fast Image Dehazing Method Based on Linear Transformation,” IEEE Transactions on Multimedia, vol. 19, no. 6, pp. 1142-1155, 2017.

K. B. Gibson and T. Q. Nguyen, “Fast single image fog removal using the adaptive Wiener filter,” in 2013 IEEE International Conference on Image Processing, 2013, pp. 714-718.

G. Ge, Z. Wei, and J. Zhao, “Fast single-image dehazing using linear transformation,” Optik - International Journal for Light and Electron Optics, vol. 126, no. 21, pp. 3245-3252, 11//2015.

S. G. Narasimhan and S. K. Nayar, “Interactive (de) weathering of an image using physical models,” in IEEE Workshop on color and photometric Methods in computer Vision, 2003, vol. 6, no. 6.4, p. 1: France.

J. Kopf et al., Deep photo: Model-based photograph enhancement and viewing (no. 5). ACM, 2008.

Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, “Polarization-based vision through haze,” Applied optics, vol. 42, no. 3, pp. 511-525, 2003.

N. Sadhvi, A. Kumari, and T. A. Sudha, “Bi-orthogonal wavelet transform based single image visibility restoration on hazy scenes,” in 2016 International Conference on Communication and Signal Processing (ICCSP), 2016, pp. 2199-2203.

M. Wang, J. Mai, Y. Liang, R. Cai, T. Zhengjia, and Z. Zhang, “Component-Based Distributed Framework for Coherent and Real-Time Video Dehazing,” in 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), 2017, vol. 1, pp. 321-324.

Y. t. Liang, L. Li, K. b. Zhao, and J. h. Hu, “Defogging algorithm of color images based on Gaussian function weighted histogram specification,” in 2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), 2016, pp. 364-369.

J. Mai, Q. Zhu, D. Wu, Y. Xie, and L. Wang, “Back propagation neural network dehazing,” in 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), 2014, pp. 1433-1438.

S. Goswami, J. Kumar, and J. Goswami, “A hybrid approach for visibility enhancement in foggy image,” in 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015, pp. 175-180.

J.-H. Yu and T.-J. Xiao, “Design and implementation of pipeline structure of image filtering process based on FPGA,” Computer Engineering and Design, vol. 30, no. 18, pp. 4192-4194, 2009.

T. Yu, I. Riaz, J. Piao, and H. Shin, “Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior,” IET Image Processing, vol. 9, no. 9, pp. 725-734, 2015.

A. G. Khodary, H. A. Aly, and Ieee, A New Image-Sequence Haze Removal System Based on DM6446 Davinci Processor (2014 Ieee Global Conference on Signal and Information Processing). 2014, pp. 703-706.

H. Koschmieder, “Theorie der horizontalen Sichtweite,” Beitrage zur Physik der freien Atmosphare, pp. 33-53, 1924.

W. Zhang, J. Liang, H. Ju, L. Ren, E. Qu, and Z. Wu, “A robust hazeremoval scheme in polarimetric dehazing imaging based on automatic identification of sky region,” Optics & Laser Technology, vol. 86, pp. 145-151, 2016.

F. Jalled and I. Voronkov, “Object Detection Using Image Processing,” arXiv preprint arXiv:1611.07791, 2016.

G. De Novi, C. Melchiorri, J. Garcia, P. Sanz, P. Ridao, and G. Oliver, “A new approach for a reconfigurable autonomous underwater vehicle for intervention,” in Systems conference, 2009 3rd annual IEEE, 2009, pp. 23-26: IEEE.

S. Liu et al., “Image de-hazing from the perspective of noise filtering,” Computers & Electrical Engineering, vol. 62, pp. 345-359, 2017.

A. Shihavuddin, N. Gracias, R. Garcia, J. Escartin, and R. B. Pedersen, “Automated classification and thematic mapping of bacterial mats in the north sea,” in OCEANS-Bergen, 2013 MTS/IEEE, 2013, pp. 1-8: IEEE.

C. Balletti, C. Beltrame, E. Costa, F. Guerra, and P. Vernier, “UNDERWATER PHOTOGRAMMETRY AND 3D RECONSTRUCTION OF MARBLE CARGOS SHIPWRECK,” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2015.

N. Aliane, J. Fernandez, M. Mata, and S. Bemposta, “A system for traffic violation detection,” Sensors, vol. 14, no. 11, pp. 22113-22127, 2014.

A. H. Ashtari, M. J. Nordin, and M. Fathy, “An Iranian license plate recognition system based on color features,” IEEE transactions on intelligent transportation systems, vol. 15, no. 4, pp. 1690-1705, 2014.

S.-C. Huang, B.-H. Chen, and Y.-J. Cheng, “An efficient visibility enhancement algorithm for road scenes captured by intelligent transportation systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 5, pp. 2321-2332, 2014.

X. Jiang, H. Yao, S. Zhang, X. Lu, and W. Zeng, “Night video enhancement using improved dark channel prior,” in ICIP, 2013, pp. 553-557.

S. M. Shankaranarayana, K. Ram, A. Vinekar, K. Mitra, and M. Sivaprakasam, “Restoration of neonatal retinal images,” 2016.

W. Rui and W. Guoyu, “Medical X-ray image enhancement method based on dark channel prior,” in Proceedings of the 5th International Conference on Bioinformatics and Computational Biology, 2017, pp. 38-41.

S. Jeong and S. Lee, “The single image dehazing based on efficient transmission estimation,” in 2013 IEEE International Conference on Consumer Electronics (ICCE), 2013, pp. 376-377.

G. Woodell, D. J. Jobson, Z.-u. Rahman, and G. Hines, “Advanced image processing of aerial imagery,” in Visual Information Processing XV, 2006, vol. 6246, p. 62460E: International Society for Optics and Photonics.

Q. Zhu, Z. Song, Y. Xie, and L. Wang, “A novel recursive Bayesian learning-based method for the efficient and accurate segmentation of video with dynamic background,” IEEE Trans. Image Processing, vol. 21, no. 9, pp. 3865-3876, 2012.

W. Liu and D. Tao, “Multiview hessian regularization for image annotation,” IEEE Transactions on Image Processing, vol. 22, no. 7, pp. 2676-2687, 2013.

Q. Zhu, Z. Zhang, Z. Song, Y. Xie, and L. Wang, “A novel nonlinear regression approach for efficient and accurate image matting,” IEEE Signal Processing Letters, vol. 20, no. 11, pp. 1078-1081, 2013.

L. Tang and G. Shao, “Drone remote sensing for forestry research and practices,” Journal of Forestry Research, vol. 26, no. 4, pp. 791-797, 2015.

H. Lu et al., “Depth map reconstruction for underwater Kinect camera using inpainting and local image mode filtering,” IEEE Access, vol. 5, pp. 7115-7122, 2017.

C. Huang, D. Yang, R. Zhang, L. Wang, and L. Zhou, “Improved algorithm for image haze removal based on dark channel priority,” Computers & Electrical Engineering, 2017.

G. Woodell, D. J. Jobson, Z.-u. Rahman, and G. Hines, “Enhancement of imagery in poor visibility conditions,” in Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IV, 2005, vol. 5778, pp. 673- 684: International Society for Optics and Photonics.

X. Ji, Y. Feng, G. Liu, M. Dai, and C. Yin, “Real-Time Defogging Processing of Aerial Images,” in 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), 2010, pp. 1-4.

Y. Qiu and S. Wu, “Contrast-based stereoscopic images dehazing,” in 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), 2015, pp. 597-602.

X. Zhang, Z. Bu, H. Chen, and M. Liu, “Fast image dehazing using joint Local Linear sure-based filter and image fusion,” in 2015 5th International Conference on Information Science and Technology (ICIST), 2015, pp. 192-197.

W. Liu, F. Zhou, T. Lu, J. Duan, and G. Qiu, “Image Defogging Quality Assessment: Real-World Database and Method,” IEEE Transactions on Image Processing, vol. 30, pp. 176-190, 2021.

X. Min et al., “Quality Evaluation of Image Dehazing Methods Using Synthetic Hazy Images,” IEEE Transactions on Multimedia, vol. 21, no. 9, pp. 2319-2333, 2019.

C. O. Ancuti, A. Kis, and C. Ancuti, “Evaluation of image dehazing techniques based on a realistic benchmark,” in 2019 International Symposium ELMAR, 2019, pp. 61-64.

X. Min, G. Zhai, K. Gu, X. Yang, and X. Guan, “Objective Quality Evaluation of Dehazed Images,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 8, pp. 2879-2892, 2019.

C. O. Ancuti, C. Ancuti, and R. Timofte, “NH-HAZE: An image dehazing benchmark with non-homogeneous hazy and haze-free images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 444-445.

P. Wang et al., “Task-driven Image Preprocessing Algorithm Evaluation Strategy,” in 2020 7th International Conference on Dependable Systems and Their Applications (DSA), 2020, pp. 500-508.

C. O. Ancuti, C. Ancuti, M. Sbert, and R. Timofte, “Dense-Haze: A Benchmark for Image Dehazing with Dense-Haze and Haze-Free Images,” in 2019 IEEE International Conference on Image Processing (ICIP), 2019, pp. 1014-1018.

P. Mahajan, V. Jakhetiya, P. Abrol, P. Lehana, B. N. Subudhi, and S. C. Guntuku, “Perceptual Quality Evaluation of Hazy Natural Images,” IEEE Transactions on Industrial Informatics, pp. 1-1, 2021.

N. A. Husain, M. S. M. Rahim, S. Kari, and H. Chaudhry, “VRHAZE: The Simulation of Synthetic Haze Based on Visibility Range for Dehazing Method in Single Image,” in 2020 6th International Conference on Interactive Digital Media (ICIDM), 2020, pp. 1-7: IEEE.

D. Berman, D. Levy, S. Avidan, T. Treibitz, "Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 8, pp. 2822-2837, 2021.

H. Li, J. Li, and W. Wang, “A fusion adversarial underwater image enhancement network with a public test dataset,” 2019.

C. Li et al., “An underwater image enhancement benchmark dataset and beyond,” vol. 29, pp. 4376-4389, 2019.

S. Zheng, J. Sun, Q. Liu, Y. Qi, and S. Zhang, “Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network,” in Proceedings of the Asian Conference on Computer Vision, 2020.

C. Ancuti, C. O. Ancuti, R. Timofte, and C. De Vleeschouwer, “I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images,” in International Conference on Advanced Concepts for Intelligent Vision Systems, 2018, pp. 620-631: Springer.

C. O. Ancuti, C. Ancuti, R. Timofte, and C. De Vleeschouwer, “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, 2018, pp. 754-762.

C. Sakaridis, D. Dai, and L. J. I. J. o. C. V. Van Gool, “Semantic foggy scene understanding with synthetic data,” vol. 126, no. 9, pp. 973-992, 2018.

J. El Khoury, J.-B. Thomas, A. J. J. o. I. S. Mansouri, and Technology, “A database with reference for image dehazing evaluation,” vol. 62, no. 1, pp. 10503-1-10503-13, 2018.

L. K. Choi, J. You, and A. C. Bovik, “Referenceless prediction of perceptual fog density and perceptual image defogging,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3888-3901, 2015.

Y. Cho, J. Jeong, A. J. I. R. Kim, and A. Letters, “Model-assisted multiband fusion for single image enhancement and applications to robot vision,” vol. 3, no. 4, pp. 2822-2829, 2018.

J. He, C. Zhang, R. Yang, and K. Zhu, “Convex optimization for fast image dehazing,” in 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 2246-2250.

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in Proceedings of the IEEE international conference on computer vision, 2013, pp. 617-624.

X. Liu, H. Zhang, Y.-m. Cheung, X. You, and Y. Y. Tang, “Efficient single image dehazing and denoising: An efficient multi-scale correlated wavelet approach,” Computer Vision and Image Understanding, vol. 162, pp. 23-33, 2017.

D. Berman and S. Avidan, “Non-local image dehazing,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 1674-1682.

J. Yu, C. Xiao, and D. Li, “Physics-based fast single image fog removal,” in IEEE 10th International Conference on Signal Processing Proceedings, 2010, pp. 1048-1052: IEEE.

R. He, Z. Wang, H. Xiong, and D. D. Feng, “Single Image Dehazing with White Balance Correction and Image Decomposition,” in 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), 2012, pp. 1-7.

K. He, J. Sun, X. J. I. t. o. p. a. Tang, and m. intelligence, “Guided image filtering,” vol. 35, no. 6, pp. 1397-1409, 2012.

C. Xiao and J. Gan, “Fast image dehazing using guided joint bilateral filter,” The Visual Computer, vol. 28, no. 6-8, pp. 713-721, 2012.

Y. Li, R. T. Tan, and M. S. Brown, “Nighttime haze removal with glow and multiple light colors,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 226-234.

J.-P. Tarel and N. Hautiere, “Fast visibility restoration from a single color or gray level image,” in Computer Vision, 2009 IEEE 12th International Conference on, 2009, pp. 2201-2208: IEEE.

A. Kumari and S. K. Sahoo, “Real Time Visibility Enhancement for Single Image Haze Removal,” Procedia Computer Science, vol. 54, pp. 501-507, 2015.

A. J. S. P. Galdran, “Image dehazing by artificial multiple-exposure image fusion,” vol. 149, pp. 135-147, 2018.

J. Zhang et al., “Image dehazing based on dark channel prior and brightness enhancement for agricultural remote sensing images from consumer-grade cameras,” vol. 151, pp. 196-206, 2018.

Y Gao, Y., Su, Y., Li, Q., & Li, J, “Single fog image restoration with multi-focus image fusion,” vol. 55, pp. 586-595, 2018.

Y. Zhu, G. Tang, X. Zhang, J. Jiang, and Q. J. N. Tian, “Haze removal method for natural restoration of images with sky,” vol. 275, pp. 499-510, 2018.

K. Kim et al., “Improvement of radiographic visibility using an image restoration method based on a simple radiographic scattering model for x-ray nondestructive testing,” vol. 98, pp. 117-122, 2018.

Zotin, A. G., “Fast algorithm of image enhancement based on multi-scale retinex,” International Journal of Reasoning-based Intelligent Systems, vol. 12, no. 2, pp. 106-116, 2020.

Q. Tang et al., “Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion,” vol. 202, p. 103086, 2021.

T. Wang, L. Zhao, P. Huang, X. Zhang, and J. J. N. Xu, “Haze concentration adaptive network for image dehazing,” vol. 439, pp. 75-85, 2021.

Y. Liu, A. Wang, H. Zhou, and P. Jia, “Single nighttime image dehazing based on image decomposition,” Signal Processing, vol. 183, p. 107986, 2021.

B. Gui, Y. Zhu, and T. Zhen, “Adaptive single image dehazing method based on support vector machine,” Journal of Visual Communication and Image Representation, vol. 70, p. 102792, 2020.

Y. Chen, B. Song, X. Du, and N. Guizani, “The enhancement of catenary image with low visibility based on multi-feature fusion network in railway industry,” Computer Communications, vol. 152, pp. 200-205, 2020.

B. Qi, C. Yang, L. Tan, X. Luo, and F. Liu, “A novel haze image steganography method via cover-source switching,” Journal of Visual Communication and Image Representation, vol. 70, p. 102814, 2020.

Y. Wu, Y. Qin, Z. Wang, X. Ma, and Z. Cao, “Densely pyramidal residual network for UAV-based railway images dehazing,” Neurocomputing, vol. 371, pp. 124-136, 2020.

K. Metwaly, X. Li, T. Guo, and V. Monga, “NonLocal Channel Attention for NonHomogeneous Image Dehazing,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, pp. 1842-1851.

A. Kumari, S. K. Sahoo, and M. C. Chinnaiah, “Fast and Efficient Visibility Restoration Technique for Single Image Dehazing and Defogging,” IEEE Access, vol. 9, pp. 48131-48146, 2021.

X. Zhao, T. Zhang, W. Chen, and W. Wu, “Image Dehazing Based on Haze Degree Classification,” in 2020 Chinese Automation Congress (CAC), 2020, pp. 4186-4191.

R. Chen, Y. Sheng, S. Wei, and D. Tang, “Research on Safe Distance Measuring Method of Front Vehicle in Foggy Environment,” in 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4), 2019, pp. 333-338.

J. Zhang, Z. Lu, and M. Li, “Active Contour-Based Method for Finger-Vein Image Segmentation,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 11, pp. 8656-8665, 2020.

A. Mehra, M. Mandal, P. Narang, and V. Chamola, “ReViewNet: A Fast and Resource Optimized Network for Enabling Safe Autonomous Driving in Hazy Weather Conditions,” IEEE Transactions on Intelligent Transportation Systems, pp. 1-11, 2020.

L.-P. Yao, Z.-l. J. M. T. Pan, and Applications, “The Retinex-based image dehazing using a particle swarm optimization method,” pp. 1-18, 2020.

I. U. Afridi, T. Bashir, H. A. Khattak, T. M. Khan, and M. Imran, “Degraded image enhancement by image dehazing and Directional Filter Banks using Depth Image based Rendering for future free-view 3D-TV,” PLOS ONE, vol. 14, no. 5, p. e0217246, 2019.

X. Wang, C. Yang, J. Zhang, H. J. I. J. o. A. Song, and B. Engineering, “Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring,” vol. 11, no. 2, pp. 170-176, 2018.

Y. Guo, J. Chen, X. Ren, A. Wang, and W. J. I. T. o. I. P. Wang, “Joint Raindrop and Haze Removal From a Single Image,” vol. 29, pp. 9508-9519, 2020.

Downloads

Published

2021-12-01
Metrics
Views/Downloads
  • Abstract
    172
  • PDF
    68

How to Cite

Hameed Abdulkareem, K., Arbaiy, N., Hussein Arif, Z., Nasser Al-Mhiqani, M., Abed Mohammed, M., Kadry, S., and Alkareem Alyasseri, Z. A. (2021). Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations. International Journal of Interactive Multimedia and Artificial Intelligence, 7(2), 172–198. https://doi.org/10.9781/ijimai.2021.11.009

Most read articles by the same author(s)