Convergence analysis of tight framelet approach for missing data recovery
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How to recover missing data from an incomplete samples is a fundamental problem in mathematics and it has wide range of applications in image analysis and processing. Although many existing methods, e.g. various data smoothing methods and PDE approaches, are available in the literature, there is always a need to find new methods leading to the best solution according to various cost functionals. In this paper, we propose an iterative algorithm based on tight framelets for image recovery from incomplete observed data. The algorithm is motivated from our framelet algorithm used in high-resolution image reconstruction and it exploits the redundance in tight framelet systems. We prove the convergence of the algorithm and also give its convergence factor. Furthermore, we derive the minimization properties of the algorithm and explore the roles of the redundancy of tight framelet systems. As an illustration of the effectiveness of the algorithm, we give an application of it in impulse noise removal.
KeywordsTight frame Missing data Inpainting Impulse noise
Mathematics Subject Classifications (2000)42C40 65T60 68U10 94A08
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- 2.Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of SIGGRAPH, pp. 417–424. New Orleans, LA (2000)Google Scholar
- 10.Chan, R., Shen, L., Shen, Z.: A framelet-based approach for image in painting. Technical Report 2005-4. The Chinese University of Hong Kong, Feb. (2005)Google Scholar
- 18.Gonzalez, R., Woods, R.: Digital Image Processing. Addison-Wesley, Boston, MA (1993)Google Scholar
- 29.Yin, L., Yang, R., Gabbouj, M., Neuvo, Y.: Weighted median filters: a tutorial. IEEE Trans. Circuit Theory 41, 157–192 (1996)Google Scholar