A Sobel-TV Based Hybrid Model for Robust Image Denoising
Affiliation(s)
Electronics & Information School of Yangtze University, Jingzhou, China. Electronic Information Engineering School of HEBI College of Vocation and Technology, Henan, China.
Electronics & Information School of Yangtze University, Jingzhou, China. Electronic Information Engineering School of HEBI College of Vocation and Technology, Henan, China.
ABSTRACT
The traditional Total-Variation algorithm has a good result to de-noise for noise image of small scale details, but it easily losses the details for the image with rich texture and tiny boundary. In order to solve this problem, this paper proposes a Sobel-TV model algorithm for image denoising. It uses TV model to de-noise and uses Sobel algorithm to control smoothness of image, which not only efficiently removes image noise but also simultaneously retail information, such as edge and texture. The experiments demonstrate that the proposed algorithm is simple, practical and generates better SNR, which is an important value to preprocess image.
Cite this paper
Tu, J. and Yang, B. (2014) A Sobel-TV Based Hybrid Model for Robust Image Denoising. Applied Mathematics, 5, 1310-1316. doi: 10.4236/am.2014.58123.
Tu, J. and Yang, B. (2014) A Sobel-TV Based Hybrid Model for Robust Image Denoising. Applied Mathematics, 5, 1310-1316. doi: 10.4236/am.2014.58123.
References
[1] Rudin, L.I., Osher, S. and Fatemi, E. (1992) Nonlinear Total Variation Noise Removal Algorithms. Physica D, 60, 259-268.
http://dx.doi.org/10.1016/0167-2789(92)90242-F [2] Aubert, G. and Vese, L. (1997) A Variantal Method in Image Recovery. SIAM Journal of Numerical Analysis, 34, 1948-1979.
http://dx.doi.org/10.1137/S003614299529230X [3] Chen, L.-X., Song, G.X., Ding, X.-H. and Wang X.-D. (2009) Improved Total Variation Algorithms to Remove Noise. Acta Photonica Sinica, 38, 1000-1004. [4] Combettes, P.L. and Pesquet, J.-C. (2004) Image Restoration Subject to a Total Variation Constraint. IEEE Transactions on Image Processing, 13, 1213-1222.
http://dx.doi.org/10.1109/TIP.2004.832922 [5] Vese, L.A. and Oshers, J. (2003) Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing. Journal of Scientific Computing, 19, 553-572.
http://dx.doi.org/10.1023/A:1025384832106 [6] Bonettini, S. and Ruggiero, V. (2012) On the Convergence of Primal-Dual Hybrid Gradient Algorithms for Total Variation Image Restoration. Journal of Mathematical Imaging and Vision, 44, 236-253. [7] Zhang, Y.D. and Wu, L.N. (2012) A Robust Hybrid Restarted Simulated Annealing Particle Swarm Optimization Technique. Advances in Computer Science and Its Applications, 1, 5-8. [8] Lysaker, M. and Tai, X.-C. (2006) Iterative Image Restoration Combining Total Variation Minimization and a Second-Order Functional. International Journal of Computer Vision, 66, 5-18.
http://dx.doi.org/10.1007/s11263-005-3219-7 [9] Michailovich, O.V. (2011) An Iterative Shrinkage Approach to Total-Variation Image Restoration. IEEE Transactions on Image Processing, 20, 1281-1299.
http://dx.doi.org/10.1109/TIP.2010.2090532
[1] Rudin, L.I., Osher, S. and Fatemi, E. (1992) Nonlinear Total Variation Noise Removal Algorithms. Physica D, 60, 259-268.
http://dx.doi.org/10.1016/0167-2789(92)90242-F [2] Aubert, G. and Vese, L. (1997) A Variantal Method in Image Recovery. SIAM Journal of Numerical Analysis, 34, 1948-1979.
http://dx.doi.org/10.1137/S003614299529230X [3] Chen, L.-X., Song, G.X., Ding, X.-H. and Wang X.-D. (2009) Improved Total Variation Algorithms to Remove Noise. Acta Photonica Sinica, 38, 1000-1004. [4] Combettes, P.L. and Pesquet, J.-C. (2004) Image Restoration Subject to a Total Variation Constraint. IEEE Transactions on Image Processing, 13, 1213-1222.
http://dx.doi.org/10.1109/TIP.2004.832922 [5] Vese, L.A. and Oshers, J. (2003) Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing. Journal of Scientific Computing, 19, 553-572.
http://dx.doi.org/10.1023/A:1025384832106 [6] Bonettini, S. and Ruggiero, V. (2012) On the Convergence of Primal-Dual Hybrid Gradient Algorithms for Total Variation Image Restoration. Journal of Mathematical Imaging and Vision, 44, 236-253. [7] Zhang, Y.D. and Wu, L.N. (2012) A Robust Hybrid Restarted Simulated Annealing Particle Swarm Optimization Technique. Advances in Computer Science and Its Applications, 1, 5-8. [8] Lysaker, M. and Tai, X.-C. (2006) Iterative Image Restoration Combining Total Variation Minimization and a Second-Order Functional. International Journal of Computer Vision, 66, 5-18.
http://dx.doi.org/10.1007/s11263-005-3219-7 [9] Michailovich, O.V. (2011) An Iterative Shrinkage Approach to Total-Variation Image Restoration. IEEE Transactions on Image Processing, 20, 1281-1299.
http://dx.doi.org/10.1109/TIP.2010.2090532