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

Danu Wiroteurairuang,Chom Kimpan,Thanwa Sripramong,Sanun Srisuk

doi:10.3844/jcssp.2014.2608.2618

Danu Wiroteurairuang, Chom Kimpan + Show 2 more

Open Access

https://doi.org/10.3844/jcssp.2014.2608.2618

Copy DOI

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.

Highlights

One of the most common topics in image processing is image denoising
Since typical noises usually increase TV of the image, Total Variation Minimization problem proposed by Rudin and Oscher (1994) meant to smooth-out the image components; which is very useful for image restoration
Conventional iterated TV denoising is not popular and considered to be inactive comparing to other techniques, it is useful in this research for it could be integrated with bilateral filtering using pixel-wise fashion

Summary

INTRODUCTION

One of the most common topics in image processing is image denoising. Many researches about image denoising were concerned with Gaussian noise reduction. In high noisy level, TV denoising outperforms bilateral filtering in terms of SNR and more details of non edges. In this research, the hybrid method is derived by encompassing two genuine methods:Bilateral filtering and Total variation methods together with K-means clustering algorithm in order to improve the denoising output resulted from a very noisy image. This hybrid method was tested and compared with its genuine parent methods-bilateral filtering and Total variation image denoising method in order to examine the denoising improvement on SNR.

Bilateral Filter

TV Denoising

K-Means Clustering

THE PROPOSED ALGORITHM

AND DISCUSSION

CONCLUSION