Abstract

Variational methods for image processing heavily rely on appropriate regularization functionals. While this topic is well investigated in the still image context, the question of suitable regularization for image sequences is still quite open, but not less important. In this talk, we present a new approach for spatio-temporal regularization of image sequences. When considering for instance the spatio-temporal Total Variation (TV) or Total Generalized Variation (TGV) functional, the scale of space with respect to time is not given a-priori and in fact defines a trade-off between spatial and temporal regularization. This can be exploited to further improve reconstruction quality by optimally balancing between two different scales via the infimal convolution of such functionals (ICTGV). We present the analysis of the resulting regularization term and its application for dynamic MRI reconstruction and the artifact-free decompression of MPEG compressed videos.