Visible to the public High density impulsive Noise removal using decision based iterated conditional modes

TitleHigh density impulsive Noise removal using decision based iterated conditional modes
Publication TypeConference Paper
Year of Publication2015
AuthorsChauhan, A. S., Sahula, V.
Conference Name2015 International Conference on Signal Processing, Computing and Control (ISPCC)
Date PublishedSept. 2015
ISBN Number978-1-4799-8436-7
Keywordscamera sensor module impairment, Cameras, Decision Based Algorithm, decision based iterated conditional modes, decision making, high density impulsive noise removal, ICM, image denoising, Image edge detection, Image quality, Image resolution, image restoration, image sensors, image transmission, impulse noise, Indexes, iterated conditional modes, iterative decision based approach, Iterative methods, iterative noisy pixel estimation, low density impulse noise, mean square error, mean square error methods, MSE, noise detection process, Noise measurement, noise removal process, peak signal to noise ratio, PSNR, pubcrawl170111, Salt & Pepper Noise, salt and pepper noise, SSIM, Structure Similarity Index, structure similarity index measure, Switches, visual communication

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).

Citation Keychauhan_high_2015