Abasnezhad Varzi, Ramzan, Vahidi, Javad, Motameni, Homayun. (1400). A real time adaptive multiresolution adaptive Wiener filter based on adaptive neuro-fuzzy inference system and fuzzy evaluation. هنرهای کاربردی, 12(1), 17-26. doi: 10.22075/ijnaa.2021.4651
Ramzan Abasnezhad Varzi; Javad Vahidi; Homayun Motameni. "A real time adaptive multiresolution adaptive Wiener filter based on adaptive neuro-fuzzy inference system and fuzzy evaluation". هنرهای کاربردی, 12, 1, 1400, 17-26. doi: 10.22075/ijnaa.2021.4651
Abasnezhad Varzi, Ramzan, Vahidi, Javad, Motameni, Homayun. (1400). 'A real time adaptive multiresolution adaptive Wiener filter based on adaptive neuro-fuzzy inference system and fuzzy evaluation', هنرهای کاربردی, 12(1), pp. 17-26. doi: 10.22075/ijnaa.2021.4651
Abasnezhad Varzi, Ramzan, Vahidi, Javad, Motameni, Homayun. A real time adaptive multiresolution adaptive Wiener filter based on adaptive neuro-fuzzy inference system and fuzzy evaluation. هنرهای کاربردی, 1400; 12(1): 17-26. doi: 10.22075/ijnaa.2021.4651

A real time adaptive multiresolution adaptive Wiener filter based on adaptive neuro-fuzzy inference system and fuzzy evaluation

International Journal of Nonlinear Analysis and Applications
مقاله 2، دوره 12، شماره 1، مرداد 2021، صفحه 17-26    اصل مقاله (1017.77 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.4651
نویسندگان
Ramzan Abasnezhad Varzi1؛ Javad Vahidi* 2؛ Homayun Motameni3
1Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran.
2Department of Mathematics, Iran University of Science and Technology, Tehran 1684613114, Iran. Department of Mathematical Sciences, University of South Africa, Pretoria 0002, South Africa.
3Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran.
چکیده
In this paper, a real-time denoising filter based on the modelling of stable hybrid models is presented. The hybrid models are composed of the shearlet filter and the adaptive Wiener filter in different forms. The optimization of various models is accomplished by the genetic algorithm. Next, regarding the significant relationship between optimal models and input images, changing the structure of optimal models for image denoising is modelled by the ANFIS. The eight hundred digital images are used as train images. For eight hundred training images, sixty-seven models are found. For integrated evaluation, the amounts of image attributes such as Peak Signal Noise Ratio, Signal Noise Ratio, Structural Similarity Index, Mean Absolute Error and Image Quality Assessment are evaluated by the Fuzzy deduction system. Finally, for the features of a sample noisy image as test data, the proposed denoising model of ANFIS is compared with wavelet filter in 2 and 4 levels, Fast bilateral filter, TV-L1, Median, shearlet filter and the adaptive Wiener filter. In addition, the run time of the proposed method is evaluated. Experiments show that the proposed method has better performance than others.
کلیدواژه‌ها
Genetic algorithm؛ denoising؛ Fuzzy deduction system؛ image processing؛ wavelet transformation؛ adaptive bilateral filters؛ adaptive neuro-fuzzy inference system
مراجع
آمار
تعداد مشاهده مقاله: 16,666
تعداد دریافت فایل اصل مقاله: 10,762
دانشگاه سمنان
پیوندهای مفید
آمار
تعداد نشریات22
تعداد شماره‌ها718
تعداد مقالات10,319
تعداد مشاهده مقاله72,323,695
تعداد دریافت فایل اصل مقاله64,046,966