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

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 others.