An Optimization Algorithm for the Removal of Impulse Noise from SAR Images using Pseudo Random Noise Masking

Uncertainties are the main difficulties of impulse noise analysis. This fact makes image denoising a difficult task. Understanding the uncertainties can improve the performance of image denoising. CM filtering and existing filtering methods like median filtering all can serve at an average level of noise that they can produce an average level of improvement in PSNR value of denoised image. The level that obtained by the methods which are currently used for noise removal does not provide enough details of the original image for SAR image noise analysis. So, still there is a requirement of an efficient filtering which can produce more related details from the original images. Pseudo random noise masking is the proposed filtering technique here which uses standard deviation and similarity parameter ‗S‘ for the removal of impulse noise from SAR images. The detection process which is related to similarity index can produce more efficient impulse noise removal which can be shown through the PSNR value comparison. The pseudo random noise masking filter (PRNM) which is designed using similarity parameter ‗S‘ can provide efficient removal of an SAR image in which above 95% of pixels were affected by impulse noise.