  Lost image areas with different size and arbitrary shape can occur in many
scenarios such as error-prone communication, depth-based image rendering or
motion compensated wavelet lifting. The goal of image reconstruction is to
restore these lost image areas as close to the original as possible. Frequency
selective extrapolation is a block-based method for efficiently reconstructing
lost areas in images. So far, the actual shape of the lost area is not
considered directly. We propose a centroid adaption to enhance the existing
frequency selective extrapolation algorithm that takes the shape of lost areas
into account. To enlarge the test set for evaluation we further propose a
method to generate arbitrarily shaped lost areas. On our large test set, we
obtain an average reconstruction gain of 1.29 dB.
