References
- Aubert, G. and Kornprobst, P. (2006). Mathematical Problems in Image Processing: Partial Differential Equation and Calculus of Variations, Springer-Verlag, New York, NY.
- Basu, M. (2002). Gaussian based edge-detection methods—A survey, IEEE Transactions on System, Man, and Cybernetics, Part C: Applications and Reviews 32(3): 252-260.
- Brezis, H. (1973). Opérateurs maximaux monotones et semigroupes de contractions dans les espaces de Hilbert, North-Holland Publishing Company, Amsterdam.
- Charbonnier, P., Blanc-Feraud, L., Aubert, G. and Barlaud, M. (1997). Deterministic edge-preserving regularization in computed imaging, IEEE Transactions on Image Processing 6(2): 298-311.
- Chen, Y. and Wunderli, T. (2002). Adaptive total variation for image restoration in BV space, Journal of Mathematical Analysis and Applications 272(3): 117-137.
- Douiri, A., Schweiger, M., Riley, J. and Arridge, S. R. (2007). Anisotropic diffusion regularization methods for diffuse optical tomography using edge prior information, Measurement Science and Technology 18(1): 87-95.
- Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence 6(6): 721-741.
- Gilboa, G. and Osher, S. (2007). Nonlocal linear image regaulrization and supervised segmentation, SIAM Journal on Multiscale Modelling and Simulation 6(2): 595-630.
- Giusti, F. (1984). Minimal Surfaces and Functions of Bounded Variation, Birkhauser, Basel.
- Lukac, R. and Smolka, B. (2003). Application of the adaptive center-weighted vector median framework for the enhancement of cDNA microarray images, International Journal of Applied Mathematics and Computer Science 13(3): 369-383.
- Perona, P. and Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7): 629-639.
- Prasath, V. B. S. and Singh, A. (2010a). A hybrid convex variational model for image restoration, Applied Mathematics and Computation 215(10): 3655-3664.
- Prasath, V. B. S. and Singh, A. (2010b). Well-posed inhomogeneous nonlinear diffusion scheme for digital image denoising, Journal of Applied Mathematics, Article ID 763847.
- Rudin, L., Osher, S. and Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms, Physica D 60 (1-4): 259-268.
- Santitissadeekorn, N. and Bollt, E. M. (1996). Image edge respecting denoising with edge denoising by a designer nonisotropic structure tensor method, Computational Methods in Applied Mathematics 9(3): 309-318.
- Strong, D. M. and Chan, T. F. (1996). Spatially and scale adaptive total variation based regularization and anisotropic diffusion in image processing, Technical Report 96-46, Computational and Applied Mathematics, University of California Los Angeles, CA ftp://ftp.math.ucla.edu/pub/camreport/cam96-46.ps.gz
- Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tapen, M. and Rother, C. (2008). A comparative study of energy minimization methods for Markov random fields with smoothness based priors, IEEE Transactions on Pattern Analysis and Machine Intelligence 30(6): 1068-1080.
- You, Y.-L. and Kaveh, M. (1999). Blind image restoration by anisotropic regularization, IEEE Transactions on Image Processing 8(3): 396-407.