Publication: Image processing with artificial intelligence
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Date
2009-04-01
Authors
Kenny, Toh Kal Vin
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Abstract
Digital images acquired through many consumer electronics products are often corrupted by salt-and-pepper noise during image acquisition, recording and transmission due to a number of nonidealities encountered in image sensors, communication channels and external disturbance. In most image processing applications, it is of vital importance to remove the noise from the image data because the performance of subsequent image processing tasks such as edge detection, image segmentation and others. This includes the elimination of salt-and-pepper noise contained in the images and at the same time preserving the image integrity. Specifically for the removal of salt-and-pepper noise, the median-based filters have been chief in this regard. Besides, there is the class of fuzzy-inference ruled by else-action (FIRE) filters, employing soft computing techniques to filter salt-and-pepper noise. In this project, a new fuzzy switching median (FSM) filter utilizing fuzzy techniques in image processing is developed. The designed filter is able to remove salt-and-pepper noise in digital images while preserving image details and textures very well. By incorporating fuzzy reasoning in correcting the detected noisy pixel, the low complexity FSMfilter is able to outperform some well known existing salt-and-pepper noise fuzzy and classical filters.