  With the availability of commercial Light Field (LF) cameras, LF imaging has
emerged as an up and coming technology in computational photography. However,
the spatial resolution is significantly constrained in commercial microlens
based LF cameras because of the inherent multiplexing of spatial and angular
information. Therefore, it becomes the main bottleneck for other applications
of light field cameras. This paper proposes an adaptation module in a
pretrained Single Image Super Resolution (SISR) network to leverage the
powerful SISR model instead of using highly engineered light field imaging
domain specific Super Resolution models. The adaption module consists of a Sub
aperture Shift block and a fusion block. It is an adaptation in the SISR
network to further exploit the spatial and angular information in LF images to
improve the super resolution performance. Experimental validation shows that
the proposed method outperforms existing light field super resolution
algorithms. It also achieves PSNR gains of more than 1 dB across all the
datasets as compared to the same pretrained SISR models for scale factor 2, and
PSNR gains 0.6 to 1 dB for scale factor 4.
