  The purpose of signal extrapolation is to estimate unknown signal parts from
known samples. This task is especially important for error concealment in image
and video communication. For obtaining a high quality reconstruction,
assumptions have to be made about the underlying signal in order to solve this
underdetermined problem. Among existent reconstruction algorithms, frequency
selective extrapolation (FSE) achieves high performance by assuming that image
signals can be sparsely represented in the frequency domain. However, FSE does
not take into account the low-pass behaviour of natural images. In this paper,
we propose a modified FSE that takes this prior knowledge into account for the
modelling, yielding significant PSNR gains.

Computer Vision and Pattern Recognition