  With the advent of smart devices that support 4K and 8K resolution, Single
Image Super Resolution (SISR) has become an important computer vision problem.
However, most super resolution deep networks are computationally very
expensive. In this paper, we propose SESR, a new class of Super-Efficient Super
Resolution networks that significantly improve image quality and reduce
computational complexity. Detailed experiments across six benchmark datasets
demonstrate that SESR achieves similar or better image quality than
state-of-the-art models while requiring 2x to 330x fewer Multiply-Accumulate
(MAC) operations. As a result, SESR can be used on constrained hardware to
perform x2 (1080p to 4K) and x4 SISR (1080p to 8K). Towards this, we simulate
hardware performance numbers for a commercial mobile Neural Processing Unit
(NPU) for 1080p to 4K (x2) and 1080p to 8K (x4) SISR. Our results highlight the
challenges faced by super resolution on AI accelerators and demonstrate that
SESR is significantly faster than existing models. Overall, SESR establishes a
new Pareto frontier on the quality (PSNR)-computation relationship for the
super resolution task.
