We propose a compressed sensing camera design that can capture high resolution light field using a DSLR. Based on a learnt light field disparity-aware dictionary, high quality light field can be reconstructed with limited number of captures.
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abstract
Light 1 field (LF) acquisition faces the challenge of extremely bulky data. Available hardware solutions usually compromise the sensor resource between spatial and angular resolutions. In this paper, a compressed sensing framework is proposed for the sampling and reconstruction of a high resolution LF based on a coded aperture camera. First, an LF dictionary based on perspective shifting is proposed for the sparse representation of the highly correlated LF. Then, two separate methods, i.e., subaperture scan and normalized fluctua10 tion, are proposed to acquire/calculate the scene disparity, which will be used during the LF reconstruction with the proposed disparity-aware dictionary. At last, a hardware implementation of the proposed LF acquisition/reconstruction scheme is carried out. Both quantitative and qualitative evaluation show that the proposed methods produce the state-of-the-art performance in both reconstruction quality and computation efficiency.