  The rapid accessibility of portable and affordable retinal imaging devices
has made early differential diagnosis easier. For example, color funduscopy
imaging is readily available in remote villages, which can help to identify
diseases like age-related macular degeneration (AMD), glaucoma, or pathological
myopia (PM). On the other hand, astronauts at the International Space Station
utilize this camera for identifying spaceflight-associated neuro-ocular
syndrome (SANS). However, due to the unavailability of experts in these
locations, the data has to be transferred to an urban healthcare facility (AMD
and glaucoma) or a terrestrial station (e.g, SANS) for more precise disease
identification. Moreover, due to low bandwidth limits, the imaging data has to
be compressed for transfer between these two places. Different super-resolution
algorithms have been proposed throughout the years to address this.
Furthermore, with the advent of deep learning, the field has advanced so much
that x2 and x4 compressed images can be decompressed to their original form
without losing spatial information. In this paper, we introduce a novel model
called Swin-FSR that utilizes Swin Transformer with spatial and depth-wise
attention for fundus image super-resolution. Our architecture achieves Peak
signal-to-noise-ratio (PSNR) of 47.89, 49.00 and 45.32 on three public
datasets, namely iChallenge-AMD, iChallenge-PM, and G1020. Additionally, we
tested the model's effectiveness on a privately held dataset for SANS provided
by NASA and achieved comparable results against previous architectures.
