A new method for impulse noise reduction from digital images Based on Adaptive Neuro-Fuzzy System and Fuzzy Wavelet Shrinkage

An Efficient Preprocessing Technique for Noise Reduction in Ear Verification System

“…A hybrid two-step method has been proposed for noise reduction from digital images, which part of it previously also has been used by these authors [14]. The proposed method at the first phase, ANFIS [12] identifies Noisy pixels with high accuracy, then FWS [27] is used based on the information of noisy and uncorrupted noisy pixels, so noisy pixel is replaced by new some value and unnoisy pixel remain naturally [28].…”

“…The main structure of proposed method for denoising is presented as figure 2. At the first stage, to noise detection, a training image with small size without any particular is used for learning, then this trained ANFIS is used for noise detection, this section of the paper already is used and described in [12,14].…”

“…In this method, a proper Fuzzy norm is used instead of the shrinkage factor [14]. As if all two neighboring around a wavelet coefficient ws,d in a location of (i, j) is contained a large (small) coefficient, then can be a sign for the proper signal (noise) is there.…”

“…These cases, the relationship between denoising and preservation of important details are very essential and require a proper algorithm for denoising. By considering the noise uncertainty in image pixels, fuzzy logic based methods [12,14,15,20,25,26] also have been increasingly proposed which often perform in three phases: image fuzzification, modification of membership values and image deffuzification. In fact, the image data transfers from the input level into the membership level and then membership values can be modified with fuzzy rule based methods.…”

“…Most of them used Median Filter (MF) methods and other versions of median filter, but MF only have a good ability to noise reduction in low noise ratio. Besides in most of the times especially in high noise ratio, MF result in destroyed vital textures and edges [13,14,15]. Recently, researchers focused on two steps of noise detection and noise replacement methods [16].…”