Journal of Computer Science

K-MEANS BASED IMAGE DENOISING USING BILATERAL FILTERING AND TOTAL VARIATION

Danu Wiroteurairuang, Sanun Srisuk, Chom Kimpan and Thanwa Sripramong

DOI : 10.3844/jcssp.2014.2608.2618

Journal of Computer Science

Volume 10, Issue 12

Pages 2608-2618

Abstract

Bilateral filter and Total variation image denoising are widely used in image denoising. In low noisy level, bilateral filtering is better than TV denoising for it reveals better SNR and sharper edges. However, in high noisy level, TV denoising outperforms bilateral filtering in terms of SNR and more details of non edges. It is very difficult to perform denoising of a very noisy image for the resulted image rarely improves its SNR comparing to the original noisy one. Even though Total variation image denoising could be used for a very noisy image, the resulted SNR still needs some improvement. In this research, the K-means-based Bilateral-TV denoising (K-BiTV) approach using pixel-wise bilateral filtering and TV denoising has been derived based on the gradient magnitude calculation of the guideline map using K-means clusters. The denoising result of K-BiTV was depended on the level of noise density and the appropriate cluster. The experimental result showed that comparing to the conventional TV denoising and bilateral filter, K-BiTV gave the higher SNRs for some images with higher level of noise density.

Copyright

© 2014 Danu Wiroteurairuang, Sanun Srisuk, Chom Kimpan and Thanwa Sripramong. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.