UPSI Digital Repository (UDRep)
|
UPSI Digital Repository (UDRep)
|
|
|
| ||||||||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| 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. ? 2020, Springer-Verlag London Ltd., part of Springer Nature. |
| References |
Abdullateef, B. N., Elias, N. F., Mohamed, H., Zaidan, A. A., & Zaidan, B. B. (2016). An evaluation and selection problems of OSS-LMS packages. SpringerPlus, 5(1), 1-35. doi:10.1186/s40064-016-1828-y Albahri, A. S., Albahri, O. S., Zaidan, A. A., Zaidan, B. B., Hashim, M., Alsalem, M. A., . . . Baqer, M. J. (2019). Based multiple heterogeneous wearable sensors: A smart real-time health monitoring structured for hospitals distributor. IEEE Access, 7, 37269-37323. doi:10.1109/ACCESS.2019.2898214 Albahri, A. S., Zaidan, A. A., Albahri, O. S., Zaidan, B. B., & Alsalem, M. A. (2018). Real-time fault-tolerant mHealth system: Comprehensive review of healthcare services, opens issues, challenges and methodological aspects. Journal of Medical Systems, 42(8) doi:10.1007/s10916-018-0983-9 Albahri, O. S., Albahri, A. S., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Mohsin, A. H., . . . Shareef, A. H. (2019). Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors. IEEE Access, 7, 50052-50080. doi:10.1109/ACCESS.2019.2910411 Alsalem, M. A., Zaidan, A. A., Zaidan, B. B., Hashim, M., Albahri, O. S., Albahri, A. S., . . . Mohammed, K. I. (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. Journal of Medical Systems, 42(11) doi:10.1007/s10916-018-1064-9 Ancuti, C., Ancuti, C. O., & De Vleeschouwer, C. (2016). D-HAZY: A dataset to evaluate quantitatively dehazing algorithms. Paper presented at the Proceedings - International Conference on Image Processing, ICIP, , 2016-August 2226-2230. doi:10.1109/ICIP.2016.7532754 Retrieved from www.scopus.com Chengtao, C., Qiuyu, Z., & Yanhua, L. (2015). A survey of image dehazing approaches. Paper presented at the Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015, 3964-3969. doi:10.1109/CCDC.2015.7162616 Retrieved from www.scopus.com Economopoulos, T. L., Asvestas, P. A., & Matsopoulos, G. K. (2010). Contrast enhancement of images using partitioned iterated function systems. Image and Vision Computing, 28(1), 45-54. doi:10.1016/j.imavis.2009.04.011 El Khoury, J., Le Moan, S., Thomas, J. -., & Mansouri, A. (2018). Color and sharpness assessment of single image dehazing. Multimedia Tools and Applications, 77(12), 15409-15430. doi:10.1007/s11042-017-5122-y Elhefnawy, E. I., Ali, H. S., & Mahmoud, I. I. (2016). Effective visibility restoration and enhancement of air polluted images with high information fidelity. Paper presented at the National Radio Science Conference, NRSC, Proceedings, , 2016-April 195-204. doi:10.1109/NRSC.2016.7450828 Retrieved from www.scopus.com Enaizan, O. (2018). Electronic medical record systems: Decision support examination framework for individual, security and privacy concerns using multi-perspective analysis. Health and Technology, , 1-28. Retrieved from www.scopus.com Goswami, S., Kumar, J., & Goswami, J. (2015). A hybrid approach for visibility enhancement in foggy image. Paper presented at the 2015 International Conference on Computing for Sustainable Global Development, INDIACom 2015, 175-180. Retrieved from www.scopus.com Guo, F., Peng, H., & Tang, J. (2016). Fast defogging and restoration assessment approach to road scene images. Journal of Information Science and Engineering, 32(3), 677-702. Retrieved from www.scopus.com Guo, F., Tang, J., & Cai, Z. -. (2014). Objective measurement for image defogging algorithms. Journal of Central South University, 21(1), 272-286. doi:10.1007/s11771-014-1938-z Guo, J. -., Syue, J. -., Radzicki, V. R., & Lee, H. (2017). An efficient fusion-based defogging. IEEE Transactions on Image Processing, 26(9), 4217-4228. doi:10.1109/TIP.2017.2706526 Hautiere, N., Tarel, J. -., Aubert, D., & Dumont, É. (2008). Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Analysis and Stereology, 27(2), 87-95. doi:10.5566/ias.v27.p87-95 Hsieh, C., Homg, S., Huang, Z., & Zhao, Q. (2017). Objective haze removal assessment based (two-objective optimization. Paper presented at the Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017, , 2018-January 279-283. doi:10.1109/ICAwST.2017.8256463 Retrieved from www.scopus.com Hsu, Y., Lee, C. -., & Kreng, V. B. (2010). The application of fuzzy delphi method and fuzzy AHP in lubricant regenerative technology selection. Expert Systems with Applications, 37(1), 419-425. doi:10.1016/j.eswa.2009.05.068 Hu, B., Li, L., Liu, H., Lin, W., & Qian, J. (2019). Pairwise-comparison-based rank learning for benchmarking image restoration algorithms. IEEE Transactions on Multimedia, 21(8), 2042-2056. doi:10.1109/TMM.2019.2894958 Hu, Z., & Liu, Q. (2014). A method for dehazed image quality assessment doi:10.1007/978-3-642-54927-4_87 Retrieved from www.scopus.com Jafari, A., Jafarian, M., Zareei, A., & Zaerpour, F. (2008). Using fuzzy delphi method in maintenance strategy selection problem. Journal of Uncertain Systems, 2(4), 289-298. Retrieved from www.scopus.com Jiang, X., Sun, J., Li, C., & Ding, H. (2018). Video image defogging recognition based on recurrent neural network. IEEE Transactions on Industrial Informatics, 14(7), 3281-3288. doi:10.1109/TII.2018.2810188 Jobson, D. J., Rahman, Z. -., Woodell, G. A., & Hines, G. D. (2006). A comparison of visual statistics for the image enhancement of FORESITE aerial images with those of major image classes. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 6246 doi:10.1117/12.664591 Retrieved from www.scopus.com Jumaah, F. M., Zaidan, A. A., Zaidan, B. B., Bahbibi, R., Qahtan, M. Y., & 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. Telecommunication Systems, 68(3), 425-443. doi:10.1007/s11235-017-0401-5 Kalid, N., Zaidan, A. A., Zaidan, B. B., Salman, O. H., Hashim, M., Albahri, O. S., & Albahri, A. S. (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. Journal of Medical Systems, 42(4) doi:10.1007/s10916-018-0916-7 Kamarulzaman, N., Jomhari, N., Raus, N. M., & Yusoff, M. Z. M. (2015). Applying the fuzzy delphi method to analyze the user requirement for user centred design process in order to create learning applications. Indian J.Set Technol, 8(32), 1-7. Retrieved from www.scopus.com 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. Retrieved from www.scopus.com Kim, K., Kim, S., & Kim, K. -. (2018). Effective image enhancement techniques for fog-affected indoor and outdoor images. IET Image Processing, 12(4), 465-471. doi:10.1049/iet-ipr.2016.0819 Kumari, A., & Sahoo, S. K. (2015). Fast single image and video deweathering using look-up-table approach. AEU - International Journal of Electronics and Communications, 69(12), 1773-1782. doi:10.1016/j.aeue.2015.09.001 Larson, E. C., & Chandler, D. M. (2010). Most apparent distortion: Full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging, 19(1) doi:10.1117/1.3267105 Lee, S., & Seo, K. -. (2016). A hybrid multi-criteria decision-making model for a cloud service selection problem using BSC, fuzzy delphi method and fuzzy AHP. Wireless Personal Communications, 86(1), 57-75. doi:10.1007/s11277-015-2976-z Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., & Wang, Z. (2019). Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, 28(1), 492-505. doi:10.1109/TIP.2018.2867951 Li, Y., You, S., Brown, M. S., & Tan, R. T. (2017). Haze visibility enhancement: A survey and quantitative benchmarking. Computer Vision and Image Understanding, 165, 1-16. doi:10.1016/j.cviu.2017.09.003 Liu, S., Rahman, M. A., Wong, C. Y., Lin, S. C. F., Jiang, G., & Kwok, N. (2015). Dark channel prior based image de-hazing: A review. Paper presented at the 2015 5th International Conference on Information Science and Technology, ICIST 2015, 345-350. doi:10.1109/ICIST.2015.7288994 Retrieved from www.scopus.com Liu, X., & Hardeberg, J. Y. (2013). Fog removal algorithms: Survey and perceptual evaluation. Paper presented at the 2013 4th European Workshop on Visual Information Processing, EUVIP 2013, 118-123. Retrieved from www.scopus.com Ma, K., Liu, W., & Wang, Z. (2015). Perceptual evaluation of single image dehazing algorithms. Paper presented at the Proceedings - International Conference on Image Processing, ICIP, , 2015-December 3600-3604. doi:10.1109/ICIP.2015.7351475 Retrieved from www.scopus.com Mai, J., Zhu, Q., & Wu, D. (2014). The latest challenges and opportunities in the current single image dehazing algorithms. Paper presented at the 2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014, 118-123. doi:10.1109/ROBIO.2014.7090317 Retrieved from www.scopus.com Malczewski, J. (1999). GIS and Multicriteria Decision Analysis, Retrieved from www.scopus.com Manakandan, S. K., Ismai, R., Jamil, M. R. M., & Ragunath, P. (2017). Pesticide applicators questionnaire content validation: A fuzzy delphi method. Medical Journal of Malaysia, 72(4), 228-235. Retrieved from www.scopus.com Morovati Sharifabadi, A., Naser Sadrabadi, A., Dehghani Bezgabadi, F., & Peirow, S. (2016). Presenting a model for evaluation and selecting suppliers using interpretive structure modeling (ISM). International Journal of Industrial Engineering & Production Research, 27(2), 109-120. Retrieved from www.scopus.com Oliveira, M., Fontes, D. B. M. M., & Pereira, T. (2014). Multicriteria decision making: A case study in the automobile industry. Ann Manag Sci, 3(1) Retrieved from www.scopus.com Pal, N. S., Lal, S., & Shinghal, K. (2018). Visibility enhancement of images degraded by hazy weather conditions using modified non-local approach. Optik, 163, 99-113. doi:10.1016/j.ijleo.2018.02.067 Pan, X., Xie, F., Jiang, Z., Shi, Z., & Luo, X. (2016). No-reference assessment on haze for remote-sensing images. IEEE Geoscience and Remote Sensing Letters, 13(12), 1855-1859. doi:10.1109/LGRS.2016.2614890 Perez, J., Sanz, P. J., Bryson, M., & Williams, S. B. (2017). A benchmarking study on single image dehazing techniques for underwater autonomous vehicles. Paper presented at the OCEANS 2017 - Aberdeen, , 2017-October 1-9. doi:10.1109/OCEANSE.2017.8084771 Retrieved from www.scopus.com Petro, A. B., Sbert, C., & Morel, J. (2014). Multiscale retinex. Image Processing on Line, 4(4), 71-88. Retrieved from www.scopus.com Petrovic-Lazarevic, S., & Abraham, A. (2004). Hybrid Fuzzy-Linear Programming Approach for Multi Criteria Decision Making Problems, Retrieved from www.scopus.com Pham, T. Y., Ma, H. M., & Yeo, G. T. (2017). Application of fuzzy delphi TOPSIS to locate logistics centers in vietnam: The logisticians’ perspective. Asian Journal of Shipping and Logistics, 33(4), 211-219. doi:10.1016/j.ajsl.2017.12.004 Rahimianzarif, E., & Moradi, M. (2018). Designing integrated management criteria of creative ideation based on fuzzy delphi analytical hierarchy process. International Journal of Fuzzy Systems, 20(3), 877-900. doi:10.1007/s40815-017-0370-6 Rahmatullah, B., Zaidan, A. A., Mohamed, F., & Sali, A. (2017). Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection. Paper presented at the 2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017, , 2017-January 1084-1088. doi:10.1109/CoDIT.2017.8102743 Retrieved from www.scopus.com Sadhvi, N., Kumari, A., & Anna Sudha, T. (2016). Bi-orthogonal wavelet transform based single image visibility restoration on hazy scenes. Paper presented at the International Conference on Communication and Signal Processing, ICCSP 2016, 2199-2203. doi:10.1109/ICCSP.2016.7754573 Retrieved from www.scopus.com Salih, M. M., Zaidan, B. B., Zaidan, A. A., & Ahmed, M. A. (2019). Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017. Computers and Operations Research, 104, 207-227. doi:10.1016/j.cor.2018.12.019 Salman, O. H., Zaidan, A. A., Zaidan, B. B., Naserkalid, & Hashim, M. (2017). Novel methodology for triage and prioritizing using big data patients with chronic heart diseases through telemedicine environmental. International Journal of Information Technology and Decision Making, 16(5), 1211-1245. doi:10.1142/S0219622017500225 Santra, S., & Chanda, B. (2016). Day/night unconstrained image dehazing. Paper presented at the Proceedings - International Conference on Pattern Recognition, , 0 1406-1411. doi:10.1109/ICPR.2016.7899834 Retrieved from www.scopus.com Senthilkumar, K. P., & Sivakumar, P. (2019). A review on haze removal techniques doi:10.1007/978-3-030-04061-1_11 Retrieved from www.scopus.com Song, W., Deng, B., Zhang, H., Xiao, Q., & Peng, S. (2016). An adaptive real-time video defogging method based on context-sensitiveness. Paper presented at the 2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016, 406-410. doi:10.1109/RCAR.2016.7784063 Retrieved from www.scopus.com Sultana, I., Ahmed, I., & Azeem, A. (2015). An integrated approach for multiple criteria supplier selection combining fuzzy delphi, fuzzy AHP and fuzzy TOPSIS. Journal of Intelligent and Fuzzy Systems, 29(4), 1273-1287. doi:10.3233/IFS-141216 Sun, W., Wang, H., Sun, C., Guo, B., Jia, W., & Sun, M. (2015). Fast single image haze removal via local atmospheric light veil estimation. Computers and Electrical Engineering, 46, 371-383. doi:10.1016/j.compeleceng.2015.02.009 Tahriri, F., Mousavi, M., Hozhabri Haghighi, S., & Zawiah Md Dawal, S. (2014). The application of fuzzy delphi and fuzzy inference system in supplier ranking and selection. Journal of Industrial Engineering International, 10(3) doi:10.1007/s40092-014-0066-6 Tariq, I., AlSattar, H. A., Zaidan, A. A., Zaidan, B. B., Abu Bakar, M. R., Mohammed, R. T., . . . Albahri, A. S. (2020). MOGSABAT: A metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Computing and Applications, 32(8), 3101-3115. doi:10.1007/s00521-018-3808-3 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. doi:10.1109/ACCESS.2018.2822775 Wang, R., & Yang, X. (2012). A fast method of foggy image enhancement. Paper presented at the Proceedings of 2012 International Conference on Measurement, Information and Control, MIC 2012, , 2 883-887. doi:10.1109/MIC.2012.6273428 Retrieved from www.scopus.com Wang, W., & Yuan, X. (2017). Recent advances in image dehazing. IEEE/CAA Journal of Automatica Sinica, 4(3), 410-436. doi:10.1109/JAS.2017.7510532 Wang, Z., & Bovik, A. C. (2002). A universal image quality index. IEEE Signal Processing Letters, 9(3), 81-84. doi:10.1109/97.995823 Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600-612. doi:10.1109/TIP.2003.819861 Whaiduzzaman, M., Gani, A., Anuar, N. B., Shiraz, M., Haque, M. N., & Haque, I. T. (2014). Cloud service selection using multicriteria decision analysis. The Scientific World Journal, 2014 doi:10.1155/2014/459375 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. doi:10.1109/ACCESS.2015.2511558 Yas, Q. M., Zadain, A. A., Zaidan, B. B., Lakulu, M. B., & 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. International Journal of Pattern Recognition and Artificial Intelligence, 31(3) doi:10.1142/S0218001417590029 Yas, Q. M., Zaidan, A. A., Zaidan, B. B., Rahmatullah, B., & Abdul Karim, H. (2018). Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions. Measurement: Journal of the International Measurement Confederation, 114, 243-260. doi:10.1016/j.measurement.2017.09.027 Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., Albahri, A. S., Yas, Q. M., & Hashim, M. (2018). A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: Coherent taxonomy, open issues and recommendation pathway solution. Health and Technology, 8(4), 223-238. doi:10.1007/s12553-018-0223-9 Zaidan, A. A., Zaidan, B. B., Al-Haiqi, A., Kiah, M. L. M., Hussain, M., & Abdulnabi, M. (2015). Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. Journal of Biomedical Informatics, 53, 390-404. doi:10.1016/j.jbi.2014.11.012 Zaidan, A. A., Zaidan, B. B., Hussain, M., Al-Haiqi, A. M., Mat Kiah, M. L., & Abdulnabi, M. (2015). Multi-criteria analysis for OS-EMR software selection problem: A comparative study. Decision Support Systems, 78, 15-27. doi:10.1016/j.dss.2015.07.002 Zaidan, B. B., & Zaidan, A. 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: Journal of the International Measurement Confederation, 117, 277-294. doi:10.1016/j.measurement.2017.12.019 Zaidan, B. B., & Zaidan, A. 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. Journal of Circuits, Systems and Computers, 26(7) doi:10.1142/S021812661750116X Zaidan, B. B., Zaidan, A. A., Abdul Karim, H., & Ahmad, N. N. (2017). A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques. International Journal of Information Technology and Decision Making, , 1-42. doi:10.1142/S0219622017500183 Zaidan, B. B., Zaidan, A. A., Karim, H. A., & Ahmad, N. 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’. Software - Practice and Experience, 47(10), 1365-1392. doi:10.1002/spe.2465 Zhang, E., Lv, K., Li, Y., & Duan, J. (2013). A fast video image defogging algorithm based on dark channel prior. Paper presented at the Proceedings of the 2013 6th International Congress on Image and Signal Processing, CISP 2013, , 1 219-223. doi:10.1109/CISP.2013.6743990 Retrieved from www.scopus.com 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. Optics and Laser Technology, 86, 145-151. doi:10.1016/j.optlastec.2016.07.015 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) doi:10.3390/en9040270 Zhu, Q., Hu, Z., & Ivanov, K. (2015). Quantitative assessment mechanism transcending visual perceptual evaluation for image dehazing. Paper presented at the 2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015, 808-813. doi:10.1109/ROBIO.2015.7418869 Retrieved from www.scopus.com Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. Graphics Gems IV, , 474-485. Retrieved from www.scopus.com |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |