Biblio

Over the past few years computer vision has become the essential aspect of modern era of technology. This computer vision is manly based on image processing whereas the image processing includes three important aspects as image filtering, image compression & image security. The image filtering can be achieved by using various filtering techniques but the PSNR & operating frequency are the most challenging aspects of image filtering. This paper mainly focused on overcoming the challenges appears while removing the salt & pepper noise with conventional median filtering by developing improved adaptive moving window architecture median filter & comparing its performance to have improved performance in terms of PSNR & operating frequency.

Salt and Pepper Noise is very common during transmission of images through a noisy channel or due to impairment in camera sensor module. For noise removal, methods have been proposed in literature, with two stage cascade various configuration. These methods, can remove low density impulse noise, are not suited for high density noise in terms of visible performance. We propose an efficient method for removal of high as well as low density impulse noise. Our approach is based on novel extension over iterated conditional modes (ICM). It is cascade configuration of two stages - noise detection and noise removal. Noise detection process is a combination of iterative decision based approach, while noise removal process is based on iterative noisy pixel estimation. Using improvised approach, up to 95% corrupted image have been recovered with good results, while 98% corrupted image have been recovered with quite satisfactory results. To benchmark the image quality, we have considered various metrics like PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error) and SSIM (Structure Similarity Index Measure).