Restoring highly corrupted images by impulse noise using radial basis functions interpolation
Fariborz Taherkhani, Mansour Jamzad · IET Image Processing · 2017
Preserving details while restoring images highly corrupted by impulsive salt and pepper noise remains a challenging problem. The authors proposed an algorithm based on radial basis functions (RBFs) interpolation which estimates the intensities of corrupted pixels by their neighbours. In this algorithm, intensity values of noisy pixels in the corrupted image are first estimated using RBFs. Next, the image is smoothed. The proposed algorithm can effectively remove the highly dense, impulsive salt and pepper noise. Experimental results show the superiority of the proposed algorithm both in noise suppression and details preservation in comparison to the recent similar methods. Extensive simulations show better results measured by peak signal‐to‐noise ratio and structural similarity index, especially when the image is corrupted by very highly dense impulse noise.