Structure tensor adaptive total variation for image restoration
Structure tensor adaptive total variation for image restoration
Image denoising and restoration is one of the basic requirements in many digital image processing systems.Variational regularization methods are widely used for removing noise without destroying edges that are important visual cues. This paper provides an adaptive version of the total variation regularization model that incorporates structure tensor eigenvalues for better edge preservation without creating blocky artifacts associated with gradient-based approaches. Experimental results on a variety of noisy images indicate that the proposed structure tensor adaptive total variation obtains promising results and compared with other methods, gets better structure preservation and robust noise removal.
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- [1] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis
and Machine Intelligence 1990; 12: 629-639. https://doi.org/10.1109/34.56205
- [2] Aubert G, Kornprobst P. Mathematical Problems in Image Processing: Partial differential Equation and Calculus
of Variations. New York, NY, USA: Springer-Verlag, 2006.
- [3] Rudin L, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D 1992; 60: 259-268. https://doi.org/10.1016/0167-2789(92)90242-F
- [4] Guo Z, Sun J Zhang, D, Wu B. Adaptive Perona-Malik model based on the variable exponent for image denoising.
IEEE Transactions on Image Processing 2012; 21: 958-967.
- [5] Prasath VBS, Vorotnikov D. Weighted and well-balanced anisotropic diffusion scheme for image denoising and restoration. Nonlinear Analysis: Real World Applications 2014; 17: 33-46.
https://doi.org/10.1016/j.nonrwa.2013.10.004
- [6] Prasath VBS, Thanh DNH, Hai NH, Cuong NX. Image restoration with total variation and iterative
regularization parameter estimation. In: The Eighth International Symposium on Information and Communication Technology (SoICT); 2017; Nha Trang, Vietnam. New York, NY, USA: ACM. pp. 378-384.
https://doi.org/10.1145/3155133.3155191
- [7] Strong DM, Chan TF. Spatially and scale adaptive total variation based regularization and anisotropic diffusion in
image processing. Technical Report 96-46, UCLA CAM, 1996.
- [8] Goldstein T, Osher S. The split Bregman algorithm for L1 regularized problems. SIAM Journal on Imaging Sciences
2009; 2: 323-343. https://doi.org/10.1137/080725891
- [9] Prasath VBS, Vorotnikov D, Pelapur R, Jose S, Seetharaman G, Palaniappan K. Multiscale Tikhonov-total variation
image restoration using spatially varying edge coherence exponent. IEEE Transactions on Image Processing 2015;
24: 5220-5235. https://doi.org/10.1109/TIP.2015.2479471
- [10] Prasath VBS. Adaptive coherence-enhancing diffusion flow for color images. Informatica 2016; 40: 337-342.
- [11] Prasath VBS, Singh A. An adaptive anisotropic diffusion scheme for image restoration and selective smoothing.
International Journal of Image and Graphics 2012; 12: 18pp. https://doi.org/10.1142/S0219467812500039
- [12] Prasath VBS. On convergent finite difference schemes for variational - PDE based image processing. Computational and Applied Mathematics 2018; 37: 1562-1580. https://doi.org/10.1007/s40314-016-0414-9
- [13] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural
similarity. IEEE Transactions on Image Processing 2004; 13: 600-612. https://doi.org/10.1109/TIP.2003.819861
- [14] Thanh, DNH, Dvoenko SD, Sang DV. A mixed noise removal method based on total variation. Informatica 2016;
40: 159-167.
- [15] Erkan U, Gökrem L. A new method based on pixel density in salt and pepper noise removal. Turk J Elec Eng &
Comp Sci 2018; 26: 162-171. https://doi.org/10.3906/elk-1705-256
- [16] Thanh DNH, Dvoenko SD. A method of total variation to remove the mixed Poisson-Gaussian noise. Pattern
Recognition and Image Analysis 2016; 26: 285-293. https://doi.org/10.1134/S1054661816020231
- [17] Thanh D, Dvoenko S, Sang D. A denoising method based on total variation. In: The Sixth International Symposium on Information and Communication Technology (SoICT); 2015; Hue City, Viet Nam. New York, NY, USA: ACM. pp. 223-230. https://doi.org/10.1145/2833258.2833281
- [18] Thanh DNH, Prasath VBS, Hieu LM. A review on CT and X-ray images denoising methods. Informatica 2019; 43:
9pp.
- [19] Prasath VBS, Singh A. Multispectral image denoising by well-posed anisotropic diffusion scheme with channel
coupling. International Journal of Remote Sensing 2010; 31: 2091-2099. https://doi.org/10.1080/01431160903260965
- [20] Moreno JC, Prasath VBS, Neves JC. Color image processing by vectorial total variation with gradient channels
coupling. Inverse Problems and Imaging 2016; 10: 461-497.