  The vast majority of photos taken today are by mobile phones. While their
quality is rapidly growing, due to physical limitations and cost constraints,
mobile phone cameras struggle to compare in quality with DSLR cameras. This
motivates us to computationally enhance these images. We extend upon the
results of Ignatov et al., where they are able to translate images from compact
mobile cameras into images with comparable quality to high-resolution photos
taken by DSLR cameras. However, the neural models employed require large
amounts of computational resources and are not lightweight enough to run on
mobile devices. We build upon the prior work and explore different network
architectures targeting an increase in image quality and speed. With an
efficient network architecture which does most of its processing in a lower
spatial resolution, we achieve a significantly higher mean opinion score (MOS)
than the baseline while speeding up the computation by 6.3 times on a
consumer-grade CPU. This suggests a promising direction for
neural-network-based photo enhancement using the phone hardware of the future.
