Abstract
The main aim of this paper is to accelerate the image decomposition model based on (BV, H −1). It is solved with a particularly effective primal-dual gradient descent algorithm. The algorithm works on the primal-dual formulation and exploits the information of the primal and dual variables simultaneously. It converges significantly faster than some popular existing methods in numerical experiments. This approach is to some extent related to projection type methods for solving variational inequalities.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Acar R., Vogel C. R. (1994) Analysis of bounded variation penalty methods for ill-posed problems. Inverse Problems 10: 1217–1229
Aujol, J.-F., Aubert, G., Blanc-Feraud, L., & Chambolle, A. (2003). Image decomposition: Application to textu-red images and SAR images. Rapport de Recherche RR 4704, INRIA, January 2003.
Aujol J.-F., Chambolle A. (2005) Dual norms and image decomposition models. International Journal of Computer Vision 63(1): 85–104
Bioucas-Dias J., Figueiredo M. (2007) Thresholding algorithms for image restoration. IEEE Transactions on Image processing 16(12): 2980–2991
Chambolle A., Lions P. L. (1997) Image recovery via total variation minimization and related problems. Numerische Mathematik 76(3): 167–188
Chambolle A. (2004) An algorithm for total variation minimization and its applications. Journal of Mathematical Imaging and Vision 20: 89–97
Chan T., Golub G., Mulet P. (1999) A nonlinear primal-dual method for total variation-based image restoration. SIAM Journal on Scientific Computing 20(6): 1964–1977
Charbonnier P., Blanc-Feraud L., Aubert G., Barlaud M. (1997) Deterministic edge-preserving regularization in computed imaging. IEEE Transactions on Image Processing 6(2): 298–311
Combettes P. L., Pesquet J. (2004) Image restoration subject to a total variation constraint. IEEE Transactions on Image Processing 13(9): 1213–1222
Darbon J., Sigelle M. (2006) Image restoration with discrete constrained total variation part I: Fast and exact optimization. Journal of Mathematical Imaging and Vision 26(3): 277–291
Daubechies I., Defrise M., De Mol C. (2004) An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics 57: 1413–1457
Daubechies I., Teschke G., Vese L. (2007) Iteratively solving linear inverse problems under general convex constraints. Inverse Problems and Imaging 1(1): 29–46
Daubechies I., Teschke G. (2004) Wavelet based image decomposition by variational functionals. Proceeding SPIE, Wavelet Applications in Industrial Processing 5266: 94–105
Dobson D., Vogel C. (1997) Convergence of an iterative method for total variation denoising. SIAM Journal on Numerical Analysis 34: 1779–1791
Hiriart-Urruty J.-B., Lemar′echal C. (1993) Convex analysis and minimization algorithms, vol. I. Springer-Verlag, Berlin & Heidelberg
Meyer, Y. (2001). Oscillating patterns in image processing and nonlinear evolution equations. Vol. 22 of University Lecture Series. American Mathematical Society, Providence, RI.
Ng M. K., Qi L., Yang Y. F., Huang Y. (2007) On semismooth Newton methods for total variation minimization. Journal of Mathematical Imaging and Vision 27(3): 265–276
Nikolova M., Chan R. (2007) The equivalence of half-quadratic minimization and the gradient linearization iteration. IEEE Transactions on Image Processing 16(6): 1623–1627
Osher S., Sole A., Vese L. (2003) Image decomposition and restoration using total variation minimization and the H −1 norm. Multiscale Modeling and Simulation 1(3): 349–370
Rudin L., Osher S., Fatemi E. (1992) Nolinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60: 59–268
Starck J.-L., Elad M., Donoho D. L. (2005) Image decomposition via the combination of sparse representations and a variational approach. IEEE Transactions Image Processing 14(10): 1570–1582
Vese L. A., Osher S. J. (2003) Modeling textures with total variation minimization and oscillating patterns in image processing. Journal of Scientific Computing 19(1–3): 553–572
Xiu N., Zhang J. (2003) Some recent advances in projection-type methods for variational inequalities. Journal of Computational and Applied Mathematics 152: 559–585
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yin, H., Liu, H. A primal-dual gradient method for image decomposition based on (BV, H −1). Multidim Syst Sign Process 22, 335–348 (2011). https://doi.org/10.1007/s11045-011-0146-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11045-011-0146-3