Abstract

In this paper, we propose a scheme based on sparse camera array and convolution neural network super-resolution for super-multiview integral imaging. In particular, the proposed scheme is adequate to not only the virtual-world three-dimensional scene with high performance and efficiency, but also the real-world 3D scene with higher availability than the traditional methods. In the proposed scheme, we first adopt the sparse camera array strategy to capture the sparse viewpoint images and use these images to synthesize the low-resolution elemental image array, then the convolution neural network super-resolution scheme is used to restore the high-resolution elemental image array from the low-resolution elemental image array for super-multiview integral image display. Experimental results verify the feasibility of the proposed scheme.

© 2019 Optical Society of America

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