Comparative results performance analysis of various filters used to remove noises in retinal images

Recently, in all image processing systems, image restoration plays a major role and it forms the major part of image processing systems. Medical images such as brain MRI images, Ultrasound images of liver and kidney and retinal images are often affected by various types of noises such as Gaussian noise and salt and pepper noise. All image restoration techniques attempts to remove various types of noises. This paper deals with various filters namely mean, averaging filter, median filter, adaptive median filter and adaptive weighted median filter for removing salt and pepper noise and Gaussian noise in retinal images. Among all the filters, adaptive weighted median filter removes the Gaussian noise and salt and pepper noise better than the other filters and the performance of all the filters are compared using metrics such as PSNR (Peak Signal to Noise ratio), MSE (Mean Square Error), NAE (Normalized Absolute Error), Normalized Cross Correlation (NK), Average Difference (AD), Maximum Difference (MD), SC (Structural content) and time elapsed to produce the denoised image. Adaptive weighted median filter gives best values for all the filters.