DICTIONARY LEARNING FOR INCOHERENT SAMPLING WITH APPLICATION TO PLENOPTIC IMAGING

We propose a method for object reconstruction from images obtained by a plenoptic camera. Our approach exploits a plenoptic system model based on diffraction analysis in order to formulate an inverse problem for object reconstruction. To solve this inverse problem, we propose a dictionary learning algorithm for signal reconstruction from measurements obtained by a deterministic linear system. In contrast to prior work in Compressive Sensing, we do not impose constraints on the measurement matrix, but allow it to be defined by the properties of a specified camera system. Given the measurement matrix, the proposed algorithm learns a dictionary from a large database of examples and simultaneously minimizes the mutual coherence between the measurement matrix and the dictionary. We evaluate the performance of the algorithm on object reconstruction from plenoptic system measurements and show that it outperforms existing solutions.