An effective 2-stage method for removing impulse noise in images

• A robust 2-stage impulse noise removal system is proposed to remove impulse noise.
• A neuro-fuzzy based impulse noise detector (NFIDET) is introduced.
• NFIDET detect impulse noise even up to 90% with zero miss and false detection rate.
• In filtering stage a robust weighted mean filter is used.
• Weights of each noise free pixel is calculated using the Geman–McClure function.

In this paper, a robust 2-stage impulse noise removal system is proposed to remove impulse noise from extremely corrupted images. The contributions are in two-fold. First, a neuro-fuzzy based impulse noise detector (NFIDET) is introduced to identify the noisy pixels. NFIDET is a powerful noise detector that can handle image corruption even up to 90% with zero miss and false detection rate with a simple neuro-fuzzy structure. This is the best result among the other impulse noise detectors in the literature. Second, this paper presents a new approach for weight calculation of adaptive weighted mean filter by using robust statistical model. An adaptive robust weighted mean (ARWM) filter removes a detected noisy pixel by adaptively determining filtering window size and replacing a noisy pixel with the weighted mean of the noise-free pixels in its window. A Geman–McClure robust estimation function is used to estimate the weights of the pixels. Simulation results also show that the proposed robust filter substantially outperforms many other existing algorithms in terms of image restoration.