A unified tight frame approach for missing data recovery in images:
In many practical problems in image processing, such as inpainting, noise removal and super-resolution image reconstruction, the observed data sets are often incomplete in the sense that features of interest in the image are missing partially or corrupted by noise. The recovery of missing data from incomplete data is an essential part of any image processing procedures whether the final image is utilized for visual interpretation or for automatic analysis. In this talk, we will discuss our new iterative algorithm for image recovery for missing data which is based on spline tight framelets. We consider in particular two main applications, namely impulse noise removal and super-resolution image reconstruction.

References:

• R. Chan, S.D. Riemenschneider, L.X. Shen, and Z.W. Shen, Tight Frame: An Efficient Way for High-Resolution Image Reconstruction, Appl. Comput. Harmon. Anal., 17 (2004), 91-115.
• R. Chan, S.D. Riemenschneider, L.X. Shen, and Z.W. Shen, High-Resolution Image Reconstruction With Displacement Errors: A Framelet Approach, Internat. J. Imaging Syst. Tech. 14 (2004), 91-104.
• R. Chan, Z. Shen and T. Xia, Resolution Enhancement for Video Clips: Tight Frame Approach, Proceedings of IEEE International Conference on Advanced Video and Signal-Based Surveillance, pp. 406-410, Como, Italy, Sept. 2005.