A cascade of filtering windows is implemented iteratively for removing random-valued impulse noise in heavily corrupted images. This method is based on the peer group concept (PGC), so a pixel is considered as noise-free if and only if for each window size, there exists a peer group of certain threshold cardinality for it. Otherwise, the pixel is considered as noisy. In the restoration process, the corrupted pixels are restored by taking the mean value of the remaining good pixels in the filtering window. Extensive simulations demonstrate that the proposed method produces competitive results at low noise rates, but at high noise rates, it outperforms other state-of-the-art methods. This approach efficiently suppresses the impulse noise, shows a low computational complexity, and has an equal effect on both color and gray-level images.
In this paper we propose a new technique to detect random-valued impulse noise in images. In this method, the noisy
pixels are detected iteratively through several phases. In each phase, a pixel will be marked as a noisy pixel if it does not
have sufficient number of similar pixels inside the neighborhood window. The size of the window increases over the
phases, so does the sufficient similar neighbor criterion. After the detection phases, all noisy pixels will be corrected in a
recovering process. We compare the performance of this method with other recently published methods, in terms of peak
signal to noise ratio and perceptual quality of the restored images. From the simulation results we observe that this
method outperforms all other methods at medium to high noise rates. The algorithm is very fast, providing consistent
performance over a wide range of noise rates. It also preserves fine details of the image.