Optimizing Mask and Dictionary of Compressive Light Field Photography

Abstract

A compressive light field camera architecture has been proposed to recover light fields from a single image. This technique has recently gained increasing interest. The reconstruction quality of light field depends on the incoherence of a projection matrix and dictionary. Compressive light field acquisition is different from acquiring traditional signal while the projection matrix has specific structural features. In this paper, we propose a new design for the mask to compute the optimized projection and a new method to optimize the dictionary for compressive light field. The contribution of this paper comprises of twofold: designing the mask to compute the optimized projection matrix from a known dictionary of compressive light field, and training the optimized dictionary when the projection is fixed. Experimental results show that our method can improve the reconstruction quality of light field views comparing with the random projection mask.