A texture image denoising model using the combination of tensor voting and total variation minimization. | [Journal of Theoretical and Applied Information Technology • 2012]

Author(s):

1. Chanjuan Liu: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China; School of Information and Electrical Engineering, Ludong University, Yantai 264025, Shandong, China

2. Xu Qian: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China

3. Caixia Li: School of Information and Electrical Engineering, Ludong University, Yantai 264025, Shandong, China

Abstract:

Combined with human vision principle, this paper firstly gives the definition of image frequency based on image local gradient and uses it to replace the image gradient in the traditional total variation (TV) model. And then tensor voting principle is introduced into the TV model and a novel texture image denoising method using the combination of tensor voting and total variation minimization is proposed. In the new model an image structure saliency function is given to replace the lagrangian multiplier in TV model, which can adjust the regularizing term and fidelity term according to the different areas of image structure features. Theoretical analysis and numerical experiment show that compared with other existing approaches the new model has an obvious anti-jamming capability and can accurately and subtly describe the sharp edges, feature structures and smooth areas, and can overcome staircase effect and over-smoothing generated by other TV models. Especially for the images with rich texture features and low signal to noise ratio (SNR), it can remove the noise while preserving significant image details and important characteristics and improve the image denoising effect. .

Page(s): 294-302

DOI: DOI not available

Published: Journal: Journal of Theoretical and Applied Information Technology, Volume: 46, Issue: 1, Year: 2012

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