Adaptive Removal of high-density salt-and-pepper noise (ARSPN) for robust ROI detection used in watermarking of MRI images of the brain

This paper presents a novel window-based method to remove high-density salt-and-pepper noise for optimal ROI (Region Of Interest) detection of brain MRI (Magnetic Resonance Imaging) images. The output of this system is used in watermarking and extracting hidden data in this type of image. In this method, for each pixel of the noisy input image, an adaptive n x n window is considered in the neighborhood of that pixel. If they are not noisy, the pixels of this window are weighted according to their distance from the desired pixel, and the weighted sum of the neighboring pixels is averaged. Then the noisy pixel replaces with the resulting value. This paper consists of three main sections: ROI detection, noise removal block, and evaluation of the proposed method against different densities of salt-and-pepper noise in the range of 1%-98%. ROI obtained by this method is the same before and after the noise. The final image has an acceptable PSNR (Peak Signal-to-Noise Ratio) for noise with various densities. Based on the experimental results obtained by the high efficient proposed noise removal method using 208 images from seven Databases (DBs), the maximum value is 61.7% for the 1% noise density and 26.4% for the 98% noise density.