A New Adaptive Filter for Removal of Impulse Noise with Uncertainty

Image Denoising remains a fundamental problem in the field of image processing. It is necessary to apply an efficient denoising technique to compensate for data corruption. In this paper, different filters such as AM, MMEM, BDND, FM, etc., are used to denoise an image. But the above filtering method does not produce good image quality when the noise level is higher than 60%. To represent the uncertainties better and resolve the image quality problems, a novel effective filter is presented based on Cloud Model (CM) for impulse noise removal. The CM filter has the better performance in image denoising across a wide range of noise levels. Even if the noise level is close to 95%, the CM filter can restore the images with good detail preservation. Comparisons of denoised images are done using the quality metrics measurements which includes Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE).