Publication Type : Conference Paper
Publisher : ICACCS 2016 - 3rd International Conference on Advanced Computing and Communication Systems: Bringing to the Table, Futuristic Technologies from Arround the Globe, Institute of Electrical and Electronics Engineers Inc.
Source : ICACCS 2016 - 3rd International Conference on Advanced Computing and Communication Systems: Bringing to the Table, Futuristic Technologies from Arround the Globe, Institute of Electrical and Electronics Engineers Inc. (2016)
ISBN : 9781467392051
Keywords : Bandpass filters, Computer vision, filter, image, Image processing, Median filters, Noise abatement, Pixels, Restoration, Salt removal, Salt-and-pepper noise, unsymmetic weights, Weighted mean
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2016
Abstract : The essential constraint on the input images to any computer vision technology is its quality. Acquiring noise free digital images is a challenge as it depends on several factors. Developing algorithms to remove noise is one way to improve the image quality. Salt and pepper noise degrades the image. The challenge here is to restore the lost information without distorting the edges. This paper introduces a new algorithm that reduces high density salt and pepper noise from images. Restoration is done by calculating the weighted mean of the nearby pixels. Weights are assigned unsymmetrically to pre-processed and unprocessed pixels. The quality was judged based on the PSNR value. The algorithm restores information for highly corrupted images. Salt and pepper noise are usually filtered with variants of the median filter. This paper provides an alternate way for noise reduction. © 2016 IEEE.
Cite this Research Publication : N. Chithirala, Natasha, B., Rubini, N., and Anisha Radhakrishnan, “Weighted Mean Filter for removal of high density Salt and Pepper noise”, in ICACCS 2016 - 3rd International Conference on Advanced Computing and Communication Systems: Bringing to the Table, Futuristic Technologies from Arround the Globe, 2016.