  Plenoptic cameras offer a cost effective solution to capture light fields by
multiplexing multiple views on a single image sensor. However, the high angular
resolution is achieved at the expense of reducing the spatial resolution of
each view by orders of magnitude compared to the raw sensor image. While light
field super-resolution is still at an early stage, the field of single image
super-resolution (SISR) has recently known significant advances with the use of
deep learning techniques. This paper describes a simple framework allowing us
to leverage state-of-the-art SISR techniques into light fields, while taking
into account specific light field geometrical constraints. The idea is to first
compute a representation compacting most of the light field energy into as few
components as possible. This is achieved by aligning the light field using
optical flows and then by decomposing the aligned light field using singular
value decomposition (SVD). The principal basis captures the information that is
coherent across all the views, while the other basis contain the high angular
frequencies. Super-resolving this principal basis using an SISR method allows
us to super-resolve all the information that is coherent across the entire
light field. This framework allows the proposed light field super-resolution
method to inherit the benefits of the SISR method used. Experimental results
show that the proposed method is competitive, and most of the time superior, to
recent light field super-resolution methods in terms of both PSNR and SSIM
quality metrics, with a lower complexity.
