A fixed-point algorithm for second-order total variation models in image denoising
- 50 Downloads
In this paper, we construct fixed-point algorithms for the second-order total variation models through discretization models and the subdifferential and proximity operators. Particularly, we focus on the convergence conditions of our algorithms by analyzing the eigenvalues of the difference matrix. The algorithms are tested on various images to verify our proposed convergence conditions. The experiments compared with the split Bregman algorithms demonstrate that fixed-point algorithms could solve the second-order functional minimization problem stably and effectively.
KeywordsFixed-point algorithm Convergence High-order total variation Image denoising
Mathematics Subject Classification68U10 65K10
- Chan TF, Esedoglu S, Park F, Yip MH (2005) Recent developments in total variation image restoration, Handbook of Mathematical Models in Computer Vision. Springer, BerlinGoogle Scholar
- Papafitsoros K, Schoenlieb CB, Sengul B (2012) Combined first and second order total variation in painting using split Bregman, IPOL. http://www.ipol.im/pub/art/2013/40/