Circulant sensing matrix optimization for efficient compressive sensing in reconstructing light field

摘要

The projection matrix in compressive light field acquisition has specific structural features that can be utilized for more efficient compressive sensing. In this paper, we propose a new mask design scheme for projection optimization in light field compressing with a novel circulant matrix structure. A learning circulant kernel algorithm is developed and applied to compute the projection matrix optimization from a known dictionary of the compressed light field. Experimental results show that the new approach can improve the reconstruction quality of the light field significantly compared with the commonly used I.I.D Guass random mask.