Title: A Unified Tight-frame Approach for Missing Data Recovery in Images

In many practical problems in image processing, 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 present our tight-frame algorithm for missing
data recovery. We start with an introduction of tight-frames. Then
we illustrate how to apply the idea to different image processing
applications such as: inpainting, impulse noise removal,
super-resolution image reconstruction, video enhancement,
and segmentation.