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Good Group Sparsity Prior for Light Field Interpolation
Shu FUJITA Keita TAKAHASHI Toshiaki FUJII
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E103A
No.1
pp.346355 Publication Date: 2020/01/01 Online ISSN: 17451337
DOI: 10.1587/transfun.2018EAP1175 Type of Manuscript: PAPER Category: Image Keyword: light field reconstruction, group sparsity, discrete Fourier transform, epipolar plane image, line structure,
Full Text: FreePDF
Summary:
A light field, which is equivalent to a dense set of multiview images, has various applications such as depth estimation and 3D display. One of the essential problems in light field applications is light field interpolation, i.e., view interpolation. The interpolation accuracy is enhanced by exploiting an inherent property of a light field. One example is that an epipolar plane image (EPI), which is a 2D subset of the 4D light field, consists of many lines, and these lines have almost the same slope in a local region. This structure induces a sparse representation in the frequency domain, where most of the energy resides on a line passing through the origin. On the basis of this observation, we propose a group sparsity prior suitable for light fields to exploit their line structure fully for interpolation. Specifically, we designed the directional groups in the discrete Fourier transform (DFT) domain so that the groups can represent the concentration of the energy, and we thereby formulated an LF interpolation problem as an overlapping group lasso. We also introduce several techniques to improve the interpolation accuracy such as applying a window function, determining group weights, expanding processing blocks, and merging blocks. Our experimental results show that the proposed method can achieve better or comparable quality as compared to stateoftheart LF interpolation methods such as convolutional neural network (CNN)based methods.

