Ehsan Miandji - Navigating Sparseland under the Restricted Isometry Property
Tid: Tor 4 december, kl 11:00-12
Plats: VR-arenan, Kopparhammaren
Talare: Ehsan Miandji
The presentation starts with a gentle introduction to the field of compressed sensing, which introduces key definitions such as the Restricted Isometry Property. Following this introduction, we present a new compressed sensing framework for reconstruction of incomplete and possibly noisy images and their higher dimensional variants, e.g. animations and light-fields. The algorithm relies on a learning-based basis representation. We train an ensemble of intrinsically two-dimensional (2D) dictionaries that operate locally on a set of 2D patches extracted from the input data. We show that one can convert the problem of 2D sparse signal recovery to an equivalent 1D form, enabling us to utilize a large family of sparse solvers. The proposed framework represents the input signals in a reduced union of subspaces model, while allowing sparsity in each subspace. Such a model leads to a much more sparse representation than widely used methods such as K-SVD. To evaluate our method, we apply it to three different scenarios where the signal dimensionality varies from 2D (images) to 3D (animations) and 4D (light-fields). We show that our method outperforms state-of-the-art algorithms in computer graphics and image processing literature.
Senast uppdaterad: Mon Dec 01 09:22:19 CET 2014