Root Mean Square filter for noisy images based on hyper graph model

Authors:
K. Kannan;B. Rajesh Kanna;C. Aravindan
Affiliations:
Department of Mathematics, SASTRA University, Thanjavur, India;Department of Computer Applications, St. Joseph's College of Engineering, Chennai, India;Department of Computer Science, SSN College of Engineering, Chennai, India
Venue:
Image and Vision Computing
Year:
2010

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Abstract

In this paper, we propose a noise removal algorithm for digital images. This algorithm is based on hypergraph model of image, which enables us to distinguish noisy pixels in the image from the noise-free ones. Hence, our algorithm obviates the need for denoising all the pixels, thereby preserving as much image details as possible. The identified noisy pixels are denoised through Root Mean Square (RMS) approximation. The performance of our algorithm, based on peak-signal-to-noise-ratio (PSNR) and mean-absolute-error (MAE), was studied on various benchmark images, and found to be superior to that of other traditional filters and other hypergraph based denoising algorithms.