Skip to main content
Log in

A comprehensive survey on image dehazing for different atmospheric scattering models

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Image dehazing techniques are widely used in complex outdoor environments and are commonly categorized based on learning mechanisms. However, the imaging mechanism reveals the reasons for the degradation of hazy images, and the imaging physics process is essential for solving clean image reconstruction. Therefore, different from the previous categorization based solely on learning mechanisms, we propose a more fundamental approach that divides the techniques based on the imaging models used and analyze the advantages and disadvantages of various imaging models to find reasonable computational methods for image reconstruction. This paper focuses on analyzing the principles of different atmospheric imaging models and discusses the dehazing methods based on these models. In addition, we also discuss the development of atmospheric scattering models and the application of different atmospheric imaging models in image dehazing. Finally, this paper presents the application effects of different atmospheric scattering models on thin fog and dense fog datasets. Various issues and challenges faced by existing image dehazing techniques are described, and further research questions are proposed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

Data available on request from the authors.

References

  1. Perkins DN (1985) The fingertip effect: How information-processing technology shapes thinking. Educ Res 14(7):11–17

    Google Scholar 

  2. Almalawi A, Khan AI, Alsolami F et al (2022) Arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model[J]. Chemosphere 303:134960

    Google Scholar 

  3. Albishry N, AlGhamdi R, Almalawi A et al (2022) An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques. Comput Intell Neurosci 2022

  4. Irshad K, Khan AI, Irfan SA et al (2020) Utilizing artificial neural network for prediction of occupants thermal comfort: A case study of a test room fitted with a thermoelectric air-conditioning system. IEEE Access 8:99709–99728

    Google Scholar 

  5. Haque A, Alshareef A, Khan AI et al (2020) Data description technique-based islanding classification for single-phase grid-connected photovoltaic system. Sensors 20(11):3320

    Google Scholar 

  6. Lu H, Li Y, Chen M et al (2018) Brain intelligence: go beyond artificial intelligence. Mobile Netw Appl 23(2):368–375

    Google Scholar 

  7. Khan AI, Alsolami F, Alqurashi F et al (2022) Novel energy management scheme in IoT enabled smart irrigation system using optimized intelligence methods. Eng Appl Artif Intell 114:104996

    Google Scholar 

  8. An S, Huang X, Cao L et al (2022) Unsupervised single image dehazing network[C]//2022 International Conference on Machine Learning and Knowledge Engineering (MLKE). IEEE: 202–206

  9. An S, Huang X, Wang L et al (2022) Semi-Supervised image dehazing network. Vis Comput 38(6):2041–2055

    Google Scholar 

  10. He X, Yan S, Hu Y et al (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Google Scholar 

  11. Kamijo S, Matsushita Y, Ikeuchi K et al (2000) Traffic monitoring and accident detection at intersections. IEEE Trans Intell Transp Syst 1(2):108–118

    Google Scholar 

  12. Papageorgiou C, Poggio T (2000) A trainable system for object detection. Int J Comput Vision 38(1):15–33

    Google Scholar 

  13. Greene JD (2016) Our driverless dilemma. Science 352(6293):1514–1515

    Google Scholar 

  14. Letaifa SB (2015) How to strategize smart cities: revealing the SMART model. J Bus Res 68(7):1414–1419

    Google Scholar 

  15. Goetz AFH, Rock BN, Rowan LC (1983) Remote sensing for exploration; an overview. Econ Geol 78(4):573–590

    Google Scholar 

  16. Karnath HO, Niemeier M, Dichgans J (1998) Space exploration in neglect. Brain: J Neurol 121(12):2357–2367

    Google Scholar 

  17. Kissinger HA (1955) Military policy and defense of the" Grey Areas". Foreign Aff 33(3):416–428

    Google Scholar 

  18. Bowers DG, Binding CE (2006) The optical properties of mineral suspended particles: A review and synthesis. Estuar Coast Shelf Sci 67(1–2):219–230

    Google Scholar 

  19. Du R, Chen C, Yang B et al (2014) Effective urban traffic monitoring by vehicular sensor networks. IEEE Trans Veh Technol 64(1):273–286

    Google Scholar 

  20. Munguía R, Urzua S, Bolea Y et al (2016) Vision-based SLAM system for unmanned aerial vehicles. Sensors 16(3):372

    Google Scholar 

  21. Wright DL, Pleasants F, Gomez-Meza M (1990) Use of advanced visual cue sources in volleyball. J Sport Exerc Psychol 12(4):406–414

    Google Scholar 

  22. Javed AR, Ur Rehman S, Khan MU et al (2021) CANintelliIDS: detecting in-vehicle intrusion attacks on a controller area network using CNN and attention-based GRU. IEEE Trans Netw Sci Eng 8(2):1456–1466

    Google Scholar 

  23. Furukawa Y (2000) Status and future direction of intelligent drive assist technology[C]//ITSC2000. 2000 IEEE Intelligent Transportation Systems. Proceedings (Cat. No. 00TH8493). IEEE: 113-118

  24. Costabile F, Birmili W, Klose S et al (2009) Spatio-temporal variability and principal components of the particle number size distribution in an urban atmosphere. Atmos Chem Phys 9(9):3163–3195

    Google Scholar 

  25. Flynn MJ, Badano A (1999) Image quality degradation by light scattering in display devices. J Digit Imaging 12(2):50–59

    Google Scholar 

  26. Hassan HM, Abdel-Aty MA (2011) Analysis of drivers’ behavior under reduced visibility conditions using a Structural Equation Modeling approach. Transport Res F: Traffic Psychol Behav 14(6):614–625

    Google Scholar 

  27. McCartney EJ (1976) Optics of the atmosphere: scattering by molecules and particles. New York

  28. Zhang J, Cao Y, Zha Z J et al (2020) Nighttime dehazing with a synthetic benchmark[C]//Proceedings of the 28th ACM International Conference on Multimedia: 2355–2363

  29. Ancuti C, Ancuti CO, De Vleeschouwer C (2016) D-hazy: A dataset to evaluate quantitatively dehazing algorithms[C]//2016 IEEE international conference on image processing (ICIP). IEEE: 2226–2230

  30. Zhang Y, Ding L, Sharma G (2017) Hazerd: an outdoor scene dataset and benchmark for single image dehazing[C]//2017 IEEE international conference on image processing (ICIP). IEEE: 3205–3209

  31. Ancuti C, Ancuti CO, Timofte R et al (2018) I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images[C]//International Conference on Advanced Concepts for Intelligent Vision Systems. Springer, Cham: 620–631

  32. Garcia-Garcia A, Orts-Escolano S, Oprea S et al (2017) A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv:1704.06857

  33. Scharstein D, Hirschmüller H, Kitajima Y et al (2014) High-resolution stereo datasets with subpixel-accurate ground truth[C]//German conference on pattern recognition. Springer, Cham: 31–42

  34. Silberman N, Hoiem D, Kohli P et al (2012) Indoor segmentation and support inference from rgbd images[C]//European conference on computer vision. Springer, Berlin, Heidelberg: 746–760

  35. Li B, Ren W, Fu D et al (2018) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28(1):492–505

    MathSciNet  Google Scholar 

  36. Ancuti CO, Ancuti C, Sbert M et al (2019) Dense-haze: A benchmark for image dehazing with dense-haze and haze-free images[C]//2019 IEEE international conference on image processing (ICIP). IEEE: 1014–1018

  37. Ancuti C O, Ancuti C, Timofte R (2020) NH-HAZE: An image dehazing benchmark with non-homogeneous hazy and haze-free images[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: 444–445

  38. Harald K (1924) Theorie der horizontalen sichtweite: Kontrast und sichtweite. Munich, Keim and Nemnich, p 12

    Google Scholar 

  39. Israël H, Kasten F (1959) Koschmieders theorie der horizontalen sichtweite[M]//Die Sichtweite im Nebel und die Möglichkeiten ihrer künstlichen Beeinflussung. VS Verlag für Sozialwissenschaften, Wiesbaden, pp 7–10

    Google Scholar 

  40. Chavez PS Jr (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24(3):459–479

    Google Scholar 

  41. Nayar SK, Narasimhan SG (1999) Vision in bad weather[C]//Proceedings of the seventh IEEE international conference on computer vision. IEEE 2:820–827

    Google Scholar 

  42. Narasimhan SG, Nayar SK (2003) Shedding light on the weather[C]//2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. IEEE 1: I-I

  43. Tan RT. Visibility in bad weather from a single image[C]//2008 IEEE conference on computer vision and pattern recognition. IEEE, 2008: 1–8

  44. He R, Wang Z, Fan Y et al. Multiple scattering model based single image dehazing[C]//2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2013: 733–737

  45. Ju M, Gu Z, Zhang D (2017) Single image haze removal based on the improved atmospheric scattering model. Neurocomputing 260:180–191

    Google Scholar 

  46. Dai C, Lin M, Wu X et al (2020) Single hazy image restoration using robust atmospheric scattering model. Signal Process 166:107257

    Google Scholar 

  47. He S, Chen Z, Wang F et al (2021) Integrated image defogging network based on improved atmospheric scattering model and attention feature fusion. Earth Sci Inf 14(4):2037–2048

    Google Scholar 

  48. Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. Int J Comput Vision 48(3):233–254

    Google Scholar 

  49. Narasimhan SG, Nayar SK (2001) Removing weather effects from monochrome images[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. IEEE, 2: II-II

  50. Narasimhan SG, Nayar SK (2000) Chromatic framework for vision in bad weather[C]//Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662). IEEE, 1: 598–605

  51. Narasimhan S, Ramamoorthi R, Nayar S (2004) Analytic rendering of multiple scattering in participating media. Submitted to ACM Trans Graph 1–28

  52. Computer Vision-ECCV 2002: 7th European Conference on Computer Vision, Copenhagen, Denmark, May 28–31, 2002. Proceedings. Part IV[M]. Springer, 2003

  53. Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell 25(6):713–724

    Google Scholar 

  54. Schechner YY, Narasimhan SG, Nayar SK (2001) Instant dehazing of images using polarization[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. IEEE, 1: I-I.

  55. Fattal R (2008) Single image dehazing. ACM Trans Graph (TOG) 27(3):1–9

    Google Scholar 

  56. Wang R, Li R, Sun H (2016) Haze removal based on multiple scattering model with superpixel algorithm. Signal Process 127:24–36

    Google Scholar 

  57. Tang C, Sun R, Lian Z et al (2021) PLSMS model for restoration of the details concealed by light sources in nighttime hazed image. SIViP 15(2):411–419

    Google Scholar 

  58. Wei P, Liu Y, Liu Y et al (2010) Dehazing model based on multiple scattering[C]//2010 3rd International Congress on Image and Signal Processing. IEEE 1:249–252

    Google Scholar 

  59. Lu X, Lv G, Lei T (2014) Single image dehazing based on multiple scattering model[C]//2014 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE: 239–244

  60. Yuan Z, Wu L, Wang Y et al (2020) Image Dehazing Based on Multiple Scattering Model[C]//Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering: 454–458

  61. Li C, Guo J, Porikli F et al (2018) A cascaded convolutional neural network for single image dehazing. IEEE Access 6:24877–24887

    Google Scholar 

  62. Song Y, Li J, Wang X et al (2017) Single image dehazing using ranking convolutional neural network. IEEE Trans Multimedia 20(6):1548–1560

    Google Scholar 

  63. Li J, Li G, Fan H (2018) Image dehazing using residual-based deep CNN. IEEE Access 6:26831–26842

    Google Scholar 

  64. Cai B, Xu X, Jia K et al (2016) Dehazenet: An end-to-end system for single image haze removal. IEEE Trans Image Process 25(11):5187–5198

    MathSciNet  Google Scholar 

  65. Yeh CH, Huang CH, Kang LW et al (2018) Single image dehazing via deep learning-based image restoration[C]//2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE: 1609–1615

  66. Wang R, You Y, Zhang Y et al (2018) Ship detection in foggy remote sensing image via scene classification R-CNN[C]//2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC). IEEE: 81–85

  67. Ren W, Pan J, Zhang H et al (2020) Single image dehazing via multi-scale convolutional neural networks with holistic edges. Int J Comput Vision 128(1):240–259

    Google Scholar 

  68. Zhang S, He F, Ren W et al (2020) Joint learning of image detail and transmission map for single image dehazing. Vis Comput 36(2):305–316

    Google Scholar 

  69. Dudhane A, Murala S (2018) C^ 2msnet: A novel approach for single image haze removal[C]//2018 IEEE winter conference on applications of computer vision (WACV). IEEE: 1397–1404

  70. Kim G, Ha S, Kwon J (2018) Adaptive patch based convolutional neural network for robust dehazing[C]//2018 25th IEEE International Conference on Image Processing (ICIP). IEEE: 2845–2849

  71. Kim M, Hong S, Kang MG (2020) Single image haze removal using multiple scattering model for road scenes. Electron Imaging 2020(16):81-1–81-6

    Google Scholar 

  72. Kim M, Hong S, Lee H et al (2021) Single image dehazing of road scenes using spatially adaptive atmospheric point spread function. IEEE Access 9:76135–76152

    Google Scholar 

  73. An S, Huang X, Zheng Z et al (2021) An end-to-end sea fog removal network using multiple scattering model. PLoS ONE 16(5):e0251337

    Google Scholar 

  74. An S, Huang X, Wang L et al (2021) Unsupervised water scene dehazing network using multiple scattering model. PLoS ONE 16(6):e0253214

    Google Scholar 

  75. An S, Huang X, Wang L et al (2021) Unsupervised single-image dehazing using the multiple-scattering model. Appl Opt 60(26):7858–7868

    Google Scholar 

  76. Yang Y, Liu C (2021) Single image dehazing using elliptic curve scattering model. SIViP 15(7):1443–1451

    Google Scholar 

  77. Huo B, Yin F (2015) Image dehazing with dark channel prior and novel estimation model. Int J Multimed Ubiquit Eng 10(3):13–22

    Google Scholar 

  78. Qiu X, Dai M, Yin C (2017) UAV remote sensing atmospheric degradation image restoration based on multiple scattering APSF estimation. Optoelectron Lett 13(5):386–391

    Google Scholar 

  79. Zheng Z, Ren W, Cao X et al (2021) Ultra-high-definition image dehazing via multi-guided bilateral learning[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE: 16180–16189

  80. Ancuti CO, Ancuti C, Timofte R et al (2018) O-haze: a dehazing benchmark with real hazy and haze-free outdoor images[C]//Proceedings of the IEEE conference on computer vision and pattern recognition workshops: 754–762

  81. Ancuti C, Ancuti CO, Timofte R (2018) Ntire 2018 challenge on image dehazing: Methods and results[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops: 891–901.

  82. Knobelreiter P, Reinbacher C, Shekhovtsov A et al (2017) End-to-end training of hybrid CNN-CRF models for stereo[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 2339–2348.

  83. Qin X, Wang Z, Bai Y et al (2020) FFA-Net: Feature fusion attention network for single image dehazing[C]//Proceedings of the AAAI Conference on Artificial Intelligence 34(07):11908–11915

  84. Fournier GR, Jonasz M (1999) Computer-based underwater imaging analysis[C]//Airborne and In-Water Underwater Imaging. Int Soc Opt Photonics 3761:62–70

    Google Scholar 

Download references

Acknowledgements

We thank the following project for funding: Shanghai Maritime University Postgraduate Top Innovative Talent Training Program. the authors would also like to sincerely thank the relevant open source websites for providing information on the dataset, and the authors would like to thank their teachers and colleagues for their valuable advice and guidance in the experimental and mathematical analysis The authors would like to thank their teachers and colleagues for their valuable advice and guidance during the experimental and mathematical analysis.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shunmin An.

Ethics declarations

Conflict of interest

Author Shunmin An declares that he has no conflict of interest. Author Linling Wang declares that she has no conflict of interest. Author Le Wang declares that he has no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

An, S., Huang, X., Cao, L. et al. A comprehensive survey on image dehazing for different atmospheric scattering models. Multimed Tools Appl 83, 40963–40993 (2024). https://doi.org/10.1007/s11042-023-17292-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17292-8

Keywords

Navigation