Adaptive Coherence-enhancing Diffusion Flow for Color Images

Authors

  • V. B. Surya Prasath University of Missouri-Columbia, USA

Abstract

Color image restoration is one of the fundamental problems in image processing pipelines. Variational regularization and diffusion partial differential equations (PDEs) are widely used in solving these low-level image smoothing and noise removal problems. In this paper, we consider a new adaptive coherence enhancing diffusion (CED) filter which combines anisotropic diffusion and structure tensor derived diffusion functions. By exploiting isotropic smoothing in homogeneous regions and anisotropic diffusion tensor filtering in edges and corners we obtain a PDE flow which can removing noise while preserving important image details. Compared to the original CED approach our proposed adaptive CED (ACED) obtains stable smoothing results. Experimental results on synthetic and real color images show that the proposed filter has good noise removal properties and quantitative measurements indicate it obtains better structure preservation as well.

Author Biography

V. B. Surya Prasath, University of Missouri-Columbia, USA

Surya Prasath is an assistant professor (research) in the Computer Science Department at the University of Missouri, USA. He received his PhD in Mathematics from the Indian Institute of Technology Madras, India in 2009 (defended in March 2010). He has been a postdoctoral fellow at the Department of Mathematics, University of Coimbra, Portugal, for two years. Since 2012 he is with the Computational Imaging and VisAnalysis (CiVA) Lab at the University of Missouri, USA working on various mathematical image processing and computer vision problems. He had summer fellowships/visits at Kitware Inc. NY, USA, The Fields Institute, Canada, and IPAM, University of California Los Angeles (UCLA), USA. His main research interests include nonlinear PDEs, regularization methods, inverse & ill-posed problems, variational, PDE based image processing, and computer vision with applications in remote sensing, medical imaging domains.

References

G. Aubert and P. Kornprobst (2006) Mathematical problems in image processing: Partial differential equation and calculus of variations, Springer-Verlag.

P. Perona and J. Malik (1990) Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 7, pp. 629–639.

D. N. H. Thanh, S. D. Dvoenko, D. V. Sang (2016) A mixed noise removal method based on total variation, Informatica, Vol. 40, pp. 159–167.

J. Weickert (1998) Anisotropic diffusion in image processing. B.G. Teubner-Verlag, Stuttgart, Germany.

J. Weickert (1999) Coherence-enhancing diffusion of colour images, Image and Vision Computing, pp. 201–212.

Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, Vol. 13, No. 4, pp. 600–612.

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Published

2016-11-03

How to Cite

Prasath, V. B. S. (2016). Adaptive Coherence-enhancing Diffusion Flow for Color Images. Informatica, 40(3). Retrieved from https://puffbird.ijs.si/index.php/informatica/article/view/1378

Issue

Vol. 40 No. 3 (2016)

Section

Regular papers

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