A multiplane image (MPI) is a useful 3-D representation composed of a stack of semi-transparent images, from which arbitrary views can be rendered with little computational cost. In this paper, we tackled the problem of super-resolution for MPIs, where a high-resolution MPI is inferred from a lower resolution one. By analyzing the anti-aliasing condition for the light field that would be produced from an MPI, we clarified that such a high-resolution MPI should have smaller sampling intervals over not only the spatial dimension but also the depth dimension. On the basis of this analysis, we constructed a learning-based method to transform a low-resolution MPI into a higher resolution one with depth resolution enhancement. Tested on BasicLFSR dataset, our method achieved 30.54 dB on average, which was 1.29 dB higher than the case without depth resolution enhancement. Visual results indicated that our method can accurately restore high-frequency components. Although super-resolution techniques have been studied extensively for images, videos, and light fields, this is the first work to address the problem of direct super-resolution for MPIs.
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