High-quality light field photography has been one of the most difficult challenges in computational photography. Conventional methods either sacrifice resolution, use multiple devices, or require multiple images to be captured. Combining coded image acquisition and compressive reconstruction is one of the most promising directions to overcome limitations of conventional light field cameras. We present a new approach to compressive light field photography that exploits a joint tensor low-rank and sparse prior (LRSP) on natural light fields. As opposed to recently proposed light field dictionaries, our method does not require a computationally expensive learning stage but rather models the redundancies of high dimensional visual signals using a tensor low-rank prior. This is not only computationally more efficient but also more flexible in that the proposed techniques are easily applicable to a wide range of different imaging systems, camera parameters, and also scene types. 
Tensor low-rank and sparse light field photography