A Real Time Adaptive Multiresolution Adaptive Wiener Filter Based On Adaptive Neuro-Fuzzy Inference System And Fuzzy evaluation

Document Type: Research Paper


1 Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran.

2 Department of Mathematics, Iran University of Science and Technology, Tehran 1684613114, Iran. Department of Mathematical Sciences, University of South Africa, Pretoria 0002, South Africa.

3 Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran.



In this paper, a real-time denoising filter based on 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 to Noise Ratio, Signal to 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 level , Fast bilateral
filter, TV-L1, Median, shearlet filter and the adaptive Wiener filter. In addition, run time of proposed
method are evaluated. Experiments show that the proposed method has better performance than