  Classic image scaling (e.g. bicubic) can be seen as one convolutional layer
and a single upscaling filter. Its implementation is ubiquitous in all display
devices and image processing software. In the last decade deep learning systems
have been introduced for the task of image super-resolution (SR), using several
convolutional layers and numerous filters. These methods have taken over the
benchmarks of image quality for upscaling tasks. Would it be possible to
replace classic upscalers with deep learning architectures on edge devices such
as display panels, tablets, laptop computers, etc.? On one hand, the current
trend in Edge-AI chips shows a promising future in this direction, with rapid
development of hardware that can run deep-learning tasks efficiently. On the
other hand, in image SR only few architectures have pushed the limit to extreme
small sizes that can actually run on edge devices at real-time. We explore
possible solutions to this problem with the aim to fill the gap between classic
upscalers and small deep learning configurations. As a transition from classic
to deep-learning upscaling we propose edge-SR (eSR), a set of one-layer
architectures that use interpretable mechanisms to upscale images. Certainly, a
one-layer architecture cannot reach the quality of deep learning systems.
Nevertheless, we find that for high speed requirements, eSR becomes better at
trading-off image quality and runtime performance. Filling the gap between
classic and deep-learning architectures for image upscaling is critical for
massive adoption of this technology. It is equally important to have an
interpretable system that can reveal the inner strategies to solve this problem
and guide us to future improvements and better understanding of larger
networks.
