SpringerLink
Log in

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

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Given the rapid development of dehazing image algorithms, selecting the optimal algorithm based on multiple criteria is crucial in determining the efficiency of an algorithm. However, a sufficient number of criteria must be considered when selecting an algorithm in multiple foggy scenes, including inhomogeneous, homogenous and dark foggy scenes. However, the selection of an optimal real-time image dehazing algorithm based on standardised criteria presents a challenge. According to previous studies, a standardisation and selection framework for real-time image dehazing algorithms based on multi-foggy scenes is not yet available. To address this gap, this study proposes a new standardisation and selection framework based on fuzzy Delphi (FDM) and hybrid multi-criteria analysis methods. Experiments are also conducted in three phases. Firstly, the image dehazing criteria are standardised based on FDM. Secondly, an evaluation experiment is conducted based on standardised criteria and nine real-time image dehazing algorithms to obtain a multi-perspective matrix. Third, entropy and VIKOR methods are hybridised to determine the weight of the standardised criteria and to rank the algorithms. Three rules are applied in the standardisation process to determine the criteria. To objectively validate the selection results, mean is applied for this purpose. The results of this work can be taken into account in designing efficient methods and metrics for image dehazing.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Liu S, Rahman M, Wong C, Lin S, Jiang G, Kwok N (2015) Dark channel prior based image de-hazing: a review. In: 2015 5th international conference on information science and technology (ICIST). IEEE, pp 345–350

  2. El Khoury J, Le Moan S, Thomas J-B, Mansouri A (2018) Color and sharpness assessment of single image dehazing. Multimed Tools Appl 77(12):15409–15430

    Google Scholar 

  3. Hu B, Li L, Liu H, Lin W, Qian J (2019) Pairwise-comparison-based rank learning for benchmarking image restoration algorithms. IEEE Trans Multimed 21(8):2042–2056

    Google Scholar 

  4. Zhu Q, Hu Z, Ivanov K (2015) Quantitative assessment mechanism transcending visual perceptual evaluation for image dehazing. In: 2015 IEEE international conference on robotics and biomimetics (ROBIO), 6–9 Dec 2015, pp 808–813. https://doi.org/10.1109/robio.2015.7418869

  5. Hu ZY, Liu Q (2014) A method for dehazed image quality assessment. In: Wen Z, Li T (eds) Practical applications of intelligent systems, Iske 2013. Advances in intelligent systems and computing, vol 279, pp 909–913

  6. Xu Y, Wen J, Fei L, Zhang Z (2016) Review of video and image defogging algorithms and related studies on image restoration and enhancement. IEEE Access 4:165–188. https://doi.org/10.1109/ACCESS.2015.2511558

    Article  Google Scholar 

  7. Wang W, Yuan X (2017) Recent advances in image dehazing. IEEE/CAA J Autom Sin 4(3):410–436. https://doi.org/10.1109/JAS.2017.7510532

    Article  MathSciNet  Google Scholar 

  8. Mai J, Zhu Q, Wu D (2014) The latest challenges and opportunities in the current single image dehazing algorithms. In: 2014 IEEE international conference on robotics and biomimetics (ROBIO 2014), 5–10 Dec 2014, pp 118–123. https://doi.org/10.1109/robio.2014.7090317

  9. Guo F, Tang J, Cai ZX (2014) Objective measurement for image defogging algorithms. J Cent South Univ 21(1):272–286. https://doi.org/10.1007/s11771-014-1938-z

    Article  Google Scholar 

  10. Liu X, Hardeberg JY (2013) Fog removal algorithms: survey and perceptual evaluation. Eur Workshop Vis Inf Process (EUVIP) 10–12(2013):118–123

    Google Scholar 

  11. Hsieh CH, Horng SC, Huang ZJ, Zhao Q (2017) Objective Haze removal assessment based on two-objective optimization. In: 2017 IEEE 8th international conference on awareness science and technology (iCAST), 8–10 Nov 2017, pp 279–283. https://doi.org/10.1109/icawst.2017.8256463

  12. Chengtao C, Qiuyu Z, Yanhua L, IEEE (2015) A survey of image dehazing approaches. In: 2015 27th Chinese control and decision conference, pp 3964–3969

  13. Wang K, Wang H, Li Y, Hu Y, Li Y (2018) Quantitative performance evaluation for dehazing algorithms on synthetic outdoor hazy images. IEEE Access 6:20481–20496

    Google Scholar 

  14. Li B et al (2019) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28(1):492–505

    MathSciNet  MATH  Google Scholar 

  15. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Graphics gems IV. Academic Press Professional, Inc., pp 474–485

  16. Petro AB, Sbert C, Morel JM (2014) Multiscale retinex. Image Processing On Line, pp 71–88

  17. Santra S, Chanda B (2016) Day/night unconstrained image dehazing. In: 2016 23rd international conference on pattern recognition (ICPR). IEEE, pp 1406–1411

  18. Jiang X, Sun J, Ding H, Li C (2018) Video image de-fogging recognition algorithm based on recurrent neural network. IEEE Trans Ind Inform 14(7):3281–3288. https://doi.org/10.1109/tii.2018.2810188

    Article  Google Scholar 

  19. Ma KD, Liu WT, Wang Z, IEEE (2015) Perceptual evaluation of single image dehazing algorithms. In: 2015 IEEE international conference on image processing, ICIP. IEEE, pp 3600–3604

  20. Senthilkumar K, Sivakumar P (2019) A review on haze removal techniques. In: Peter JD, Fernandes SL, Thomaz CE, Viriri S (eds) Computer aided intervention and diagnostics in clinical and medical images. Springer, Berlin, pp 113–123

    Google Scholar 

  21. Hautière N, Tarel J-P, Aubert D, Dumont E (2011) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereol 27(2):87–95

    MathSciNet  MATH  Google Scholar 

  22. Jafari A, Jafarian M, Zareei A, Zaerpour F (2008) Using fuzzy Delphi method in maintenance strategy selection problem. J Uncertain Syst 2(4):289–298

    Google Scholar 

  23. Khatami Firoozabadi A, Bamdad Soofi J, Taheri F, Salehi M (2009) Presentation decision support system inconjunction with supplier selection and evaluation using the UTA method. J Manag Dev 13–88

  24. Sultana I, Ahmed I, Azeem A (2015) An integrated approach for multiple criteria supplier selection combining Fuzzy Delphi, Fuzzy AHP and Fuzzy TOPSIS. J Intell Fuzzy Syst 29(4):1273–1287

    MathSciNet  MATH  Google Scholar 

  25. Kumari A, Sahoo SK (2015) Fast single image and video deweathering using look-up-table approach. AEU Int J Electron Commun 69(12):1773–1782. https://doi.org/10.1016/j.aeue.2015.09.001

    Article  Google Scholar 

  26. Sun W, Wang H, Sun CH, Guo BL, Jia WY, Sun MG (2015) Fast single image haze removal via local atmospheric light veil estimation. Comput Electr Eng 46:371–383. https://doi.org/10.1016/j.compeleceng.2015.02.009

    Article  Google Scholar 

  27. Pal NS, Lal S, Shinghal K (2018) Visibility enhancement of images degraded by hazy weather conditions using modified non-local approach. Optik 163:99–113. https://doi.org/10.1016/j.ijleo.2018.02.067

    Article  Google Scholar 

  28. Rong W, **aoGang Y (2012) A fast method of foggy image enhancement. In: Proceedings of 2012 international conference on measurement, information and control, vol 2, 18–20 May 2012, pp 883–887. https://doi.org/10.1109/mic.2012.6273428

  29. Pan XX, **e FY, Jiang ZG, Shi ZW, Luo XY (2016) No-reference assessment on haze for remote-sensing images. IEEE Geosci Remote Sens Lett 13(12):1855–1859. https://doi.org/10.1109/lgrs.2016.2614890

    Article  Google Scholar 

  30. Elhefnawy EI, Ali HS, Mahmoud II, (2016) Effective visibility restoration and enhancement of air polluted images with high information fidelity. In: ElKhamy S, ElBadawy H, ElDiasty S (eds) 2016 33rd National radio science conference, NRSC, pp 195–204

  31. Jobson DJ, Rahman Z-U, Woodell GA, Hines GD (2006) A comparison of visual statistics for the image enhancement of foresite aerial images with those of major image classes. In: Rahman Z, Reichenbach SE, Neifeld MA (eds) Visual information processing XV, vol 6246. International Society for Optics and Photonics, Bellingham, p 624601

    Google Scholar 

  32. Economopoulos TL, Asvestas PA, Matsopoulos GK (2010) Contrast enhancement of images using partitioned iterated function systems. Image Vis Comput 28(1):45–54

    Google Scholar 

  33. Zhang E, Lv K, Li Y, Duan J (2013) A fast video image defogging algorithm based on dark channel prior. In: 2013 6th International congress on image and signal processing (CISP), vol 01, 18 Dec 2013, 18 Dec. 2013, pp 219–223. https://doi.org/10.1109/cisp.2013.6743990

  34. Guo F, Peng H, Tang J (2016) Fast defogging and restoration assessment approach to road scene images. J Inf Sci Eng 32(3):677–702

    Google Scholar 

  35. Larson EC, Chandler DM (2010) Most apparent distortion: full-reference image quality assessment and the role of strategy. J Electron Imaging 19(1):011006

    Google Scholar 

  36. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Google Scholar 

  37. Wang Z, Bovik AC (2002) A universal image quality index. IEEE Signal Process Lett 9(3):81–84

    Google Scholar 

  38. Perez J, Sanz PJ, Bryson M, Williams SB (2017) A benchmarking study on single image dehazing techniques for underwater autonomous vehicles. In: OCEANS 2017—Aberdeen, 19–22 June 2017, pp 1–9. https://doi.org/10.1109/oceanse.2017.8084771

  39. Kim K, Kim S, Kim K-S (2017) Effective image enhancement techniques for fog-affected indoor and outdoor images. IET Image Proc 12(4):465–471

    Google Scholar 

  40. Sadhvi N, Kumari A, Sudha TA (2016) Bi-orthogonal wavelet transform based single image visibility restoration on hazy scenes. In: 2016 International conference on communication and signal processing (ICCSP), 6–8 April 2016, pp 2199–2203. https://doi.org/10.1109/iccsp.2016.7754573

  41. Song W, Deng B, Zhang H, **ao Q, Peng S (2016) An adaptive real-time video defogging method based on context-sensitiveness. In: 2016 IEEE international conference on real-time computing and robotics (RCAR), 6–10 June 2016, pp 406–410. https://doi.org/10.1109/rcar.2016.7784063

  42. Guo JM, Syue JY, Radzicki VR, Lee H (2017) An efficient fusion-based defogging. IEEE Trans Image Process 26(9):4217–4228. https://doi.org/10.1109/tip.2017.2706526

    Article  MathSciNet  MATH  Google Scholar 

  43. Li Y, You S, Brown MS, Tan RT (2017) Haze visibility enhancement: a survey and quantitative benchmarking. Comput Vis Image Underst 165:1–16. https://doi.org/10.1016/j.cviu.2017.09.003

    Article  Google Scholar 

  44. Ancuti C, Ancuti CO, Vleeschouwer CD (2016) D-HAZY: a dataset to evaluate quantitatively dehazing algorithms. In: 2016 IEEE international conference on image processing (ICIP), 25–28 Sept 2016, pp 2226–2230. https://doi.org/10.1109/icip.2016.7532754

  45. Goswami S, Kumar J, Goswami J (2015) A hybrid approach for visibility enhancement in foggy image. In: 2015 2nd International conference on computing for sustainable global development (INDIACom), 11–13 March 2015, pp 175–180

  46. Zhang W, Liang J, Ju H, Ren L, Qu E, Wu Z (2016) A robust haze-removal scheme in polarimetric dehazing imaging based on automatic identification of sky region. Opt Laser Technol 86:145–151. https://doi.org/10.1016/j.optlastec.2016.07.015

    Article  Google Scholar 

  47. Pham TY, Ma HM, Yeo GT (2017) Application of Fuzzy Delphi TOPSIS to locate logistics centers in Vietnam: the Logisticians’ perspective. Asian J Ship** Logist 33(4):211–219

    Google Scholar 

  48. Sharifabadi AM, Sadrabadi AN, Bezegabadi FD, Peirow S, Taki E (2015) Presenting a model for evaluation and selecting suppliers using interpretive structure modeling (ISM). Int J Acad Res 27(2):109–120

    Google Scholar 

  49. Kamarulzaman N, Jomhari N, Raus NM, Yusoff MZM (2015) Applying the fuzzy delphi method to analyze the user requirement for user centred design process in order to create learning applications. Indian J Sci Technol 8(32):1–17

    Google Scholar 

  50. Rahimianzarif E, Moradi M (2018) Designing integrated management criteria of creative ideation based on fuzzy delphi analytical hierarchy process. Int J Fuzzy Syst 20(3):877–900

    Google Scholar 

  51. Manakandan SK, Rosnah I, Mohd JR, Priya R (2017) Pesticide applicators questionnaire content validation: a fuzzy delphi method. Med J Malays 72(4):228–235

    Google Scholar 

  52. Zhao H, Li N (2016) Optimal siting of charging stations for electric vehicles based on fuzzy Delphi and hybrid multi-criteria decision making approaches from an extended sustainability perspective. Energies 9(4):270

    Google Scholar 

  53. Hsu Y-L, Lee C-H, Kreng VB (2010) The application of Fuzzy Delphi method and Fuzzy AHP in lubricant regenerative technology selection. Expert Syst Appl 37(1):419–425

    Google Scholar 

  54. Lee S, Seo K-K (2016) A hybrid multi-criteria decision-making model for a cloud service selection problem using BSC, fuzzy Delphi method and fuzzy AHP. Wireless Pers Commun 86(1):57–75

    Google Scholar 

  55. Tahriri F, Mousavi M, Haghighi SH, Dawal SZM (2014) The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection. J Ind Eng Int 10(3):66

    Google Scholar 

  56. Alsalem M et al (2018) Systematic review of an automated multiclass detection and classification system for acute Leukaemia in terms of evaluation and benchmarking, open challenges, issues and methodological aspects. J Med Syst 42(11):204

    Google Scholar 

  57. Albahri A, Zaidan A, Albahri O, Zaidan B, Alsalem M (2018) Real-time fault-tolerant mhealth system: comprehensive review of healthcare services, opens issues, challenges and methodological aspects. J Med Syst 42(8):137

    Google Scholar 

  58. Yas QM, Zaidan A, Zaidan B, Rahmatullah B, Karim HA (2017) Comprehensive insights into evaluation and benchmarking of real-time skin detectors: review, open issues & challenges, and recommended solutions. Measurement 114:243–260

    Google Scholar 

  59. Whaiduzzaman M, Gani A, Anuar NB, Shiraz M, Haque MN, Haque IT (2014) Cloud service selection using multicriteria decision analysis. Sci World J 2014:1–10

  60. Kalid N et al (2018) Based on real time remote health monitoring systems: a new approach for prioritization “large scales data” patients with chronic heart diseases using body sensors and communication technology. J Med Syst 42(4):69

    Google Scholar 

  61. Albahri A et al (2019) Based multiple heterogeneous wearable sensors: a smart real-time health monitoring structured for hospitals distributor. IEEE Access 7:37269–37323

    Google Scholar 

  62. Albahri O et al (2019) Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors. IEEE Access 7:50052–50080

    Google Scholar 

  63. Zaidan A et al (2018) A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution. Health Technol 8:1–16

    Google Scholar 

  64. Petrovic-Lazarevic S, Abraham A (2004) Hybrid fuzzy-linear programming approach for multi criteria decision making problems. ar**v preprint cs/0405019

  65. Zaidan A, Zaidan B, Al-Haiqi A, Kiah MLM, Hussain M, Abdulnabi M (2015) Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. J Biomed Inform 53:390–404

    Google Scholar 

  66. Zaidan A, Zaidan B, Hussain M, Haiqi A, Kiah MM, Abdulnabi M (2015) Multi-criteria analysis for OS-EMR software selection problem: a comparative study. Decis Support Syst 78:15–27

    Google Scholar 

  67. Abdullateef BN, Elias NF, Mohamed H, Zaidan A, Zaidan B (2016) An evaluation and selection problems of OSS-LMS packages. SpringerPlus 5(1):248

    Google Scholar 

  68. Yas QM, Zadain A, Zaidan B, Lakulu M, Rahmatullah B (2017) Towards on develop a framework for the evaluation and benchmarking of skin detectors based on artificial intelligent models using multi-criteria decision-making techniques. Int J Pattern Recognit Artif Intell 31(03):1759002

    Google Scholar 

  69. Zaidan B, Zaidan A, Karim HA, Ahmad N (2017) A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi-criteria analysis based on ‘large-scale data’. Softw Pract Exp 47(10):1365–1392

    Google Scholar 

  70. Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, New York

    Google Scholar 

  71. Zaidan B, Zaidan A (2017) Software and hardware FPGA-based digital watermarking and steganography approaches: toward new methodology for evaluation and benchmarking using multi-criteria decision-making techniques. J Circuits Syst Comput 26(07):1750116

    Google Scholar 

  72. Zaidan B, Zaidan A, Abdul Karim H, Ahmad N (2017) A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques. Int J Inf Technol Decis Mak 16:1–42

  73. Jumaah F, Zaidan A, Zaidan B, Bahbibi R, Qahtan M, Sali A (2018) Technique for order performance by similarity to ideal solution for solving complex situations in multi-criteria optimization of the tracking channels of GPS baseband telecommunication receivers. Telecommun Syst 68(3):425–443

    Google Scholar 

  74. Rahmatullah B, Zaidan A, Mohamed F, Sali A (2017) Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection. In: 2017 4th international conference on control, decision and information technologies (CoDIT), 2017. IEEE, pp 1084–1088

  75. Salman OH, Zaidan A, Zaidan B, Naserkalid A, Hashim M (2017) Novel methodology for triage and prioritizing using “big data” patients with chronic heart diseases through telemedicine environmental. Int J Inf Technol Decis Mak 16(05):1211–1245

    Google Scholar 

  76. Zaidan B, Zaidan A (2018) Comparative study on the evaluation and benchmarking information hiding approaches based multi-measurement analysis using TOPSIS method with different normalisation, separation and context techniques. Measurement 117:277–294

    Google Scholar 

  77. Oliveira M, Fontes DB, Pereira T (2014) Multicriteria decision making: a case study in the automobile industry. Ann Manag Sci 3(1):109

    Google Scholar 

  78. AlSattar H et al (2018) MOGSABAT: a metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Comput Appl 32:1–15

  79. Enaizan O et al (2018) Electronic medical record systems: decision support examination framework for individual, security and privacy concerns using multi-perspective analysis. Health Technol 1–28

  80. Salih MM, Zaidan B, Zaidan A, Ahmed MA (2019) Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017. Comput Oper Res 104:207–227

    MathSciNet  MATH  Google Scholar 

  81. Kalid N, Zaidan A, Zaidan B, Salman OH, Hashim M, Muzammil H (2018) Based real time remote health monitoring systems: a review on patients prioritization and related “big data” using body sensors information and communication technology. J Med Syst 42(2):30

    Google Scholar 

  82. Jumaah F, Zadain A, Zaidan B, Hamzah A, Bahbibi R (2018) Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment. Measurement 118:83–95

    Google Scholar 

  83. Albahri O, Zaidan A, Zaidan B, Hashim M, Albahri A, Alsalem M (2018) Real-time remote health-monitoring Systems in a Medical Centre: a review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. J Med Syst 42(9):164

    Google Scholar 

  84. Zaidan A, Zaidan B, Alsalem M, Albahri O, Albahri A, Qahtan M (2019) Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04325-3

  85. Albahri O et al (2018) Systematic review of real-time remote health monitoring system in triage and priority-based sensor technology: taxonomy, open challenges, motivation and recommendations. J Med Syst 42(5):80

    Google Scholar 

  86. Lim C, Tan K, Zaidan A, Zaidan B (2020) A proposed methodology of bringing past life in digital cultural heritage through crowd simulation: a case study in George Town, Malaysia. Multimed Tools Appl 79(5):3387–3423

    Google Scholar 

  87. Napi N, Zaidan A, Zaidan B, Albahri O, Alsalem M, Albahri A (2019) Medical emergency triage and patient prioritisation in a telemedicine environment: a systematic review. Health Technol 9:1–22

  88. Jadhav A, Sonar R (2009) Analytic hierarchy process (AHP), weighted scoring method (WSM), and hybrid knowledge based system (HKBS) for software selection: a comparative study. In: 2009 Second international conference on emerging trends in engineering and technology. IEEE, pp 991–997

  89. Khatari M, Zaidan A, Zaidan B, Albahri O, Alsalem M (2019) Multi-criteria evaluation and benchmarking for active queue management methods: open issues challenges and recommended pathway solutions. Int J Inf Technol Decis Mak 18(4):1187–1242

    Google Scholar 

  90. Almahdi E, Zaidan A, Zaidan B, Alsalem M, Albahri O, Albahri A (2019) Mobile patient monitoring systems from a benchmarking aspect: challenges, open issues and recommended solutions. J Med Syst 43(7):207

    Google Scholar 

  91. Almahdi E, Zaidan A, Zaidan B, Alsalem M, Albahri O, Albahri A (2019) Mobile-based patient monitoring systems: a prioritisation framework using multi-criteria decision-making techniques. J Med Syst 43(7):219

    Google Scholar 

  92. Mohammed K et al (2019) Real-time remote-health monitoring systems: a review on patients prioritisation for multiple-chronic diseases, taxonomy analysis, concerns and solution procedure. J Med Syst 43(7):223

    Google Scholar 

  93. Alaa M et al (2019) Assessment and ranking framework for the English skills of pre-service teachers based on fuzzy Delphi and TOPSIS methods. IEEE Access 7:126201–126223

    Google Scholar 

  94. Ibrahim N et al (2019) Multi-criteria evaluation and benchmarking for Young learners’ english language mobile applications in terms of LSRW skills. IEEE Access 7:146620–146651

    Google Scholar 

  95. Talal M et al (2019) Comprehensive review and analysis of anti-malware apps for smartphones. Telecommun Syst 72(2):285–337

    Google Scholar 

  96. Nedher A-S, Hassan S, Katuk N (2014) On multi attribute decision making methods: prioritizing information security controls. J Appl Sci 14(16):1865–1870

    Google Scholar 

  97. Mohammed K et al (2020) Novel technique for reorganisation of opinion order to interval levels for solving several instances representing prioritisation in patients with multiple chronic diseases. Comput Methods Progr Biomed 185:105151

    Google Scholar 

  98. Hongjiu L, Yanrong H (2015) An evaluating method with combined assigning-weight based on maximizing variance. Sci Program 2015:3

    Google Scholar 

  99. Zou Z-H, Yi Y, Sun J-N (2006) Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. J Environ Sci 18(5):1020–1023

    Google Scholar 

  100. Opricovic S, Tzeng G-H (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455

    MATH  Google Scholar 

  101. Opricovic S, Tzeng G-H (2007) Extended VIKOR method in comparison with outranking methods. Eur J Oper Res 178(2):514–529

    MATH  Google Scholar 

  102. Mahjouri M, Ishak MB, Torabian A, Manaf LA, Halimoon N, Ghoddusi J (2017) Optimal selection of iron and steel wastewater treatment technology using integrated multi-criteria decision-making techniques and fuzzy logic. Process Saf Environ Prot 107:54–68

    Google Scholar 

  103. Shemshadi A, Shirazi H, Toreihi M, Tarokh MJ (2011) A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Syst Appl 38(10):12160–12167

    Google Scholar 

  104. Malekian A, Azarnivand A (2016) Application of integrated Shannon’s entropy and VIKOR techniques in prioritization of flood risk in the Shemshak watershed, Iran. Water Resour Manag 30(1):409–425

    Google Scholar 

  105. Bhuyan R, Routara B (2016) Optimization the machining parameters by using VIKOR and entropy weight method during EDM process of Al-18% SiCp metal matrix composite. Decis Sci Lett 5(2):269–282

    Google Scholar 

  106. Mohsen O, Fereshteh N (2017) An extended VIKOR method based on entropy measure for the failure modes risk assessment: a case study of the geothermal power plant (GPP). Saf Sci 92:160–172

    Google Scholar 

  107. Mardani A, Zavadskas EK, Govindan K, Amat Senin A, Jusoh A (2016) VIKOR technique: a systematic review of the state of the art literature on methodologies and applications. Sustainability 8(1):37

    Google Scholar 

  108. Cheng C-H, Lin Y (2002) Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. Eur J Oper Res 142(1):174–186

    MATH  Google Scholar 

  109. Chu H-C, Hwang G-J (2008) A Delphi-based approach to develo** expert systems with the cooperation of multiple experts. Expert Syst Appl 34(4):2826–2840

    Google Scholar 

  110. Murry JW Jr, Hammons JO (1995) Delphi: a versatile methodology for conducting qualitative research. Rev Higher Educ 18(4):423–436

    Google Scholar 

  111. Bekri RM, Ruhizan M, Norazah M, Nur YFA, Ashikin HT (2013) Development of Malaysia skills certificate E-portfolio: a conceptual framework. Proc Soc Behav Sci 103:323–329

    Google Scholar 

  112. Bodjanova S (2006) Median alpha-levels of a fuzzy number. Fuzzy Sets Syst 157(7):879–891

    MathSciNet  MATH  Google Scholar 

  113. Tang C-W, Wu C-T (2010) Obtaining a picture of undergraduate education quality: a voice from inside the university. High Educ 60(3):269–286

    Google Scholar 

  114. 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. https://doi.org/10.1109/TIP.2015.2456502

    Article  MathSciNet  MATH  Google Scholar 

  115. 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. https://doi.org/10.1109/TIP.2016.2598681

    Article  MathSciNet  MATH  Google Scholar 

  116. Ren W, Liu S, Zhang H, Pan J, Cao X, Yang M-H (2016) Single image dehazing via multi-scale convolutional neural networks. In: European conference on computer vision. Springer, pp 154–169

  117. Salazar-Colores S, Cruz-Aceves I, Ramos-Arreguin J-M (2018) Single image dehazing using a multilayer perceptron. J Electron Imaging 27(4):043022

    Google Scholar 

  118. Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24(11):3522–3533

    MathSciNet  MATH  Google Scholar 

  119. Cho Y, Jeong J, Kim A (2018) Model-assisted multiband fusion for single image enhancement and applications to robot vision. IEEE Robot Autom Lett 3(4):2822–2829

    Google Scholar 

  120. He J, Zhang C, Yang R, Zhu K (2016) Convex optimization for fast image dehazing. In: 2016 IEEE international conference on image processing (ICIP), 25–28 Sept 2016, pp 2246–2250. https://doi.org/10.1109/icip.2016.7532758

  121. Meng G, Wang Y, Duan J, **ang S, Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization. In: Proceedings of the IEEE international conference on computer vision, pp 617–624

  122. Liu X, Zhang H, Cheung Y-M, You X, Tang YY (2017) Efficient single image dehazing and denoising: an efficient multi-scale correlated wavelet approach. Comput Vis Image Underst 162:23–33. https://doi.org/10.1016/j.cviu.2017.08.002

    Article  Google Scholar 

  123. Berman D, Avidan S (2016) Non-local image dehazing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1674–1682

  124. Lotfi FH, Fallahnejad R (2010) Imprecise Shannon’s entropy and multi attribute decision making. Entropy 12(1):53–62

    MATH  Google Scholar 

  125. Wu J, Sun J, Liang L, Zha Y (2011) Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Syst Appl 38(5):5162–5165

    Google Scholar 

  126. Alsalem M et al (2019) Multiclass benchmarking framework for automated acute Leukaemia detection and classification based on BWM and group-VIKOR. J Med Syst 43(7):212

    Google Scholar 

  127. Triantaphyllou E, Baig K (2005) The impact of aggregating benefit and cost criteria in four MCDA methods. IEEE Trans Eng Manag 52(2):213–226

    Google Scholar 

  128. Mullen PM (2003) Delphi: myths and reality. J Health Organ Manag 17(1):37–52

    Google Scholar 

  129. Bueno S, Salmeron JL (2008) Fuzzy modeling enterprise resource planning tool selection. Comput Stand Interfaces 30(3):137–147

    Google Scholar 

  130. Manoliadis O, Tsolas I, Nakou A (2006) Sustainable construction and drivers of change in Greece: a Delphi study. Constr Manag Econ 24(2):113–120

    Google Scholar 

  131. Mohamad SNA, Embi MA, Nordin N (2015) Determining e-Portfolio elements in learning process using fuzzy Delphi analysis. Int Educ Stud 8(9):171–176

    Google Scholar 

  132. Chang P-L, Hsu C-W, Chang P-C (2011) Fuzzy Delphi method for evaluating hydrogen production technologies. Int J Hydrog Energy 36(21):14172–14179

    Google Scholar 

  133. Qader M, Zaidan B, Zaidan A, Ali S, Kamaluddin M, Radzi W (2017) A methodology for football players selection problem based on multi-measurements criteria analysis. Measurement 111:38–50

    Google Scholar 

  134. Wu D, Zhu Q, Wang J, **e Y, Wang L (2014) Image haze removal: status, challenges and prospects. In: 2014 4th IEEE international conference on information science and technology, 26–28 April 2014, pp 492–497. https://doi.org/10.1109/icist.2014.6920524

  135. Duarte A, Codevilla F, Gaya JDO, Botelho SSC (2016) A dataset to evaluate underwater image restoration methods. In: OCEANS 2016—Shanghai, 10–13 April 2016, pp 1–6. https://doi.org/10.1109/oceansap.2016.7485524

  136. Li Y, Wang K, Xu N, Li Y (2017) Quantitative evaluation for dehazing algorithms on synthetic outdoor hazy dataset. In: 2017 IEEE visual communications and image processing (VCIP), 10–13 Dec 2017, pp 1–4. https://doi.org/10.1109/vcip.2017.8305081

  137. Yadav G, Maheshwari S, Agarwal A (2014) Fog removal techniques from images: a comparative review and future directions. In: 2014 international conference on signal propagation and computer technology (ICSPCT 2014), 12–13 July 2014, pp 44–52. https://doi.org/10.1109/icspct.2014.6884973

  138. Pal T, Bhowmik MK, Bhattacharjee D, Ghosh AK (2016) Visibility enhancement techniques for fog degraded images: a comparative analysis with performance evaluation. In: 2016 IEEE region 10 conference (TENCON), 22–25 Nov 2016, pp 2583–2588. https://doi.org/10.1109/tencon.2016.7848504

  139. Roy SD, Bhowmik MK, Saha SS (2017) 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), 3–5 July 2017, pp 1–7. https://doi.org/10.1109/icccnt.2017.8204002

  140. Wang W, Chang F, Ji T, Wu X (2018) A fast single-image dehazing method based on a physical model and gray projection. IEEE Access 6:5641–5653. https://doi.org/10.1109/ACCESS.2018.2794340

    Article  Google Scholar 

  141. Chen BH, Huang SC, Cheng FC (2016) A high-efficiency and high-speed gain intervention refinement filter for haze removal. J Disp Technol 12(7):753–759. https://doi.org/10.1109/JDT.2016.2518646

    Article  Google Scholar 

  142. Zhang T, Hu HM, Li B (2018) A naturalness preserved fast dehazing algorithm using HSV color space. IEEE Access. https://doi.org/10.1109/access.2018.2806372

    Article  Google Scholar 

  143. Khodary AG, Aly HA, IEEE (2014) A new image-sequence haze removal system based on DM6446 Davinci processor. In: 2014 IEEE global conference on signal and information processing, pp 703–706

  144. El-Hashash MM, Aly HA, Mahmoud TA, Swelam W (2015) A video haze removal system on heterogeneous cores. In: 2015 IEEE global conference on signal and information processing (GlobalSIP), pp 1255–1259. https://doi.org/10.1109/globalsip.2015.7418399

  145. Changli L, Tanghuai F, **ao M, Zhen Z, Hongxin W, Lin C (2017) An improved image defogging method based on dark channel prior. In: 2017 2nd international conference on image, vision and computing (ICIVC), 2–4 June 2017, pp 414–417. https://doi.org/10.1109/icivc.2017.7984589

  146. Guo F, Cai Z, **e B, Tang J (2010) Automatic image haze removal based on luminance component. In: 2010 6th International conference on wireless communications networking and mobile computing (WiCOM), 23–25 Sept 2010, pp 1–4. https://doi.org/10.1109/wicom.2010.5600632

  147. Mai J, Zhu Q, Wu D, **e Y, Wang (2014) Back propagation neural network dehazing. In: 2014 IEEE international conference on robotics and biomimetics (ROBIO 2014), 5–10 Dec, pp 1433–1438. https://doi.org/10.1109/robio.2014.7090535

  148. Nair D, Sankaran P (2018) Color image dehazing using surround filter and dark channel prior. J Vis Commun Image Represent 50:9–15

    Google Scholar 

  149. Yadav G, Maheshwari S, Agarwal A (2014) Contrast limited adaptive histogram equalization based enhancement for real time video system. In: 2014 International conference on advances in computing, communications and informatics (ICACCI), 24–27 Sept 2014, pp 2392–2397. https://doi.org/10.1109/icacci.2014.6968381

  150. Roy K, Kumar S, Banerjee S, Sarkar TS, Chaudhuri SS (2017) 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), 28–29 April 2017, pp 1–6. https://doi.org/10.1109/iementech.2017.8076989

  151. Ancuti CO, Ancuti C, Bekaert P (2010) Effective single image dehazing by fusion. In: 2010 IEEE international conference on image processing, 26–29 Sept 2010, pp 3541–3544. https://doi.org/10.1109/icip.2010.5651263

  152. Liao B, Yin P, **ao C (2018) Efficient image dehazing using boundary conditions and local contrast. Comput Graph 70:242–250. https://doi.org/10.1016/j.cag.2017.07.016

    Article  Google Scholar 

  153. Negru M, Nedevschi S, Peter RI (2015) Exponential contrast restoration in fog conditions for driving assistance. IEEE Trans Intell Transp Syst 16(4):2257–2268. https://doi.org/10.1109/TITS.2015.2405013

    Article  Google Scholar 

  154. Negru M, Nedevschi S, Peter RI (2014) Exponential image enhancement in daytime fog conditions. In: 17th International IEEE conference on intelligent transportation systems (ITSC), 8–11 Oct 2014, pp 1675–1681. https://doi.org/10.1109/itsc.2014.6957934

  155. Kumari A, Kodati H, Sahoo SK (2015) 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), 14–17 Dec 2015, 14–17 Dec 2015, pp 1–5. https://doi.org/10.1109/wci.2015.7495527

  156. Qian X, Han L (2014) Fast image dehazing algorithm based on multiple filters. In: 2014 10th international conference on natural computation (ICNC), 19–21 Aug 2014, pp 937–0941. https://doi.org/10.1109/icnc.2014.6975965

  157. Wang W, Yuan X, Wu X, Liu Y (2017) Fast image dehazing method based on linear transformation. IEEE Trans Multimed 19(6):1142–1155. https://doi.org/10.1109/TMM.2017.2652069

    Article  Google Scholar 

  158. Zhang X, Bu Z, Chen H, Liu M (2015) Fast image dehazing using joint Local Linear sure-based filter and image fusion. In 2015 5th international conference on information science and technology (ICIST), 24–26 Apr 2015, pp 192–197. https://doi.org/10.1109/icist.2015.7288966

  159. Zhu X, Li Y, Qiao Y (2015) Fast single image dehazing through Edge-Guided Interpolated Filter. In: 2015 14th IAPR international conference on machine vision applications (MVA), 18–22 May 2015, pp 443–446. https://doi.org/10.1109/mva.2015.7153106

  160. Zhang B, Zhao J (2017) Hardware implementation for real-time haze removal. IEEE Trans Very Large Scale Integr VLSI Syst 25(3):1188–1192. https://doi.org/10.1109/tvlsi.2016.2622404

    Article  Google Scholar 

  161. Zhao X, Ding W, Liu C, Li H (2018) Haze removal for unmanned aerial vehicle aerial video based on spatial-temporal coherence optimisation. IET Image Proc 12(1):88–97. https://doi.org/10.1049/iet-ipr.2017.0060

    Article  Google Scholar 

  162. Liu S et al (2017) Image de-hazing from the perspective of noise filtering. Comput Electr Eng 62:345–359. https://doi.org/10.1016/j.compeleceng.2016.11.021

    Article  Google Scholar 

  163. Huang C, Yang D, Zhang R, Wang L, Zhou L (2017) Improved algorithm for image haze removal based on dark channel priority. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.09.018

    Article  Google Scholar 

  164. **e B, Guo F, Cai Z (2010) Improved Single Image Dehazing Using Dark Channel Prior and Multi-scale Retinex. In 2010 international conference on intelligent system design and engineering application, 13–14 Oct 2010, vol 1, pp 848–851. https://doi.org/10.1109/isdea.2010.141

  165. Zhi W, Watabe D, Jianting C (2016) Improving visibility of a fast dehazing method. In 2016 world automation congress (WAC), July 31 2016–Aug 4 2016, pp 1-6. https://doi.org/10.1109/wac.2016.7582960

  166. Liu H, Huang D, Hou S, Yue R (2017) Large size single image fast defogging and the real time video defogging FPGA architecture. Neurocomputing 269:97–107. https://doi.org/10.1016/j.neucom.2016.09.139

    Article  Google Scholar 

  167. Hautiere N, Tarel JP, Aubert D (2010) Mitigation of visibility loss for advanced camera-based driver assistance. IEEE Trans Intell Transp Syst 11(2):474–484. https://doi.org/10.1109/TITS.2010.2046165

    Article  Google Scholar 

  168. Kumari A, Sahoo SK (2015) Real time visibility enhancement for single image haze removal. Procedia Comput Sci 54:501–507. https://doi.org/10.1016/j.procs.2015.06.057

    Article  Google Scholar 

  169. Zhang J, Ding Y, Yang Y, Sun J (2016) Real-time defog model based on visible and near-infrared information. In: 2016 IEEE international conference on multimedia & expo workshops (ICMEW), 11–15 July 2016, pp 1–6. https://doi.org/10.1109/icmew.2016.7574749

  170. Ji X, Feng Y, Liu G, Dai M, Yin C (2010) Real-time defogging processing of aerial images. In: 2010 6th international conference on wireless communications networking and mobile computing (WiCOM), 23–25 Sept 2010, pp 1–4. https://doi.org/10.1109/wicom.2010.5600245

  171. Alajarmeh A, Salam RA, Abdulrahim K, Marhusin MF, Zaidan AA, Zaidan BB (2018) Real-time framework for image dehazing based on linear transmission and constant-time airlight estimation. Inf Sci 436–437:108–130. https://doi.org/10.1016/j.ins.2018.01.009

    Article  MathSciNet  Google Scholar 

  172. Yu T, Riaz I, Piao J, Shin H (2015) Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior. IET Image Proc 9(9):725–734. https://doi.org/10.1049/iet-ipr.2015.0087

    Article  Google Scholar 

  173. Liu X, Zhang H, Tang YY, Du JX (2016) Scene-adaptive single image dehazing via opening dark channel model. IET Image Proc 10(11):877–884. https://doi.org/10.1049/iet-ipr.2016.0138

    Article  Google Scholar 

  174. Liu X, Zeng F, Huang Z, Ji Y (2013) Single color image dehazing based on digital total variation filter with color transfer. In: 2013 IEEE international conference on image processing, 15–18 Sept 2013, pp 909–913. https://doi.org/10.1109/icip.2013.6738188

  175. Huang D, Chen K, Lu, J, Wang W (2017) Single image dehazing based on deep neural network. In: 2017 international conference on computer network, electronic and automation (ICCNEA), 23–25 Sept 2017, pp 294–299. https://doi.org/10.1109/iccnea.2017.107

  176. Ancuti CO, Ancuti C (2013) Single image Dehazing by multi-scale fusion. IEEE Trans Image Process 22(8):3271–3282. https://doi.org/10.1109/TIP.2013.2262284

    Article  Google Scholar 

  177. Bui TM, Kim W (2018) Single image dehazing using color ellipsoid prior. IEEE Trans Image Process 27(2):999–1009. https://doi.org/10.1109/TIP.2017.2771158

    Article  MathSciNet  MATH  Google Scholar 

  178. Riaz I, Yu T, Rehman Y, Shin H (2016) Single image dehazing via reliability guided fusion. J Vis Commun Image Represent 40(Part A):85–97. https://doi.org/10.1016/j.jvcir.2016.06.011

    Article  Google Scholar 

  179. Zhao H, **ao C, Yu J, Xu X (2015) Single image fog removal based on local extrema. IEEE/CAA J Autom Sin 2(2):158–165. https://doi.org/10.1109/JAS.2015.7081655

    Article  MathSciNet  Google Scholar 

  180. Tripathi AK, Mukhopadhyay S (2012) Single image fog removal using anisotropic diffusion. IET Image Proc 6(7):966–975. https://doi.org/10.1049/iet-ipr.2011.0472

    Article  MathSciNet  Google Scholar 

  181. Gao Z, Bai Y (2016) Single image haze removal algorithm using pixel-based airlight constraints. In: 2016 22nd international conference on automation and computing (ICAC), 7–8 Sept 2016, pp 267–272. https://doi.org/10.1109/iconac.2016.7604930

  182. Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40(1):41–50. https://doi.org/10.1016/j.compeleceng.2013.10.016

    Article  Google Scholar 

  183. **e B, Guo F, Cai ZX (2012) Universal strategy for surveillance video defogging. Opti Eng 51(10), Art no. 101703. https://doi.org/10.1117/1.oe.51.10.101703

  184. Shiau YH, Kuo YT, Chen PY, Hsu FY (2017) VLSI design of an efficient flicker-free video defogging method for real-time applications. IEEE Trans Circuits Syst Video Technol PP(99):1. https://doi.org/10.1109/tcsvt.2017.2777140

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia

    A. A. Zaidan, B. B. Zaidan & O. S. Albahri

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia

    Karrar Hameed Abdulkareem & Nureize Arbaiy

  3. College of Agriculture, Al-Muthanna University, Samawah, 66001, Iraq

    Karrar Hameed Abdulkareem

  4. College of Administration and Economic, University of Mosul, Mosul, Iraq

    M. A. Alsalem

  5. Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, 34001, Iraq

    Mahmood M. Salih

Authors
  1. Karrar Hameed Abdulkareem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nureize Arbaiy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. A. Zaidan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. B. B. Zaidan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. O. S. Albahri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. A. Alsalem
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mahmood M. Salih
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. A. Zaidan.

Additional information

Publisher's Note

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

Appendix

Appendix

See Table 11.

Table 11 Classification and frequency of usage for image dehazing criteria
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdulkareem, K.H., Arbaiy, N., Zaidan, A.A. 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 Comput & Applic 33, 1029–1054 (2021). https://doi.org/10.1007/s00521-020-05020-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05020-4

Keywords

Search

Navigation