  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 Super-Efficient Super Resolution (SESR)
networks that establish a new state-of-the-art for efficient super resolution.
Our approach is based on linear overparameterization of CNNs and creates an
efficient model architecture for SISR. With theoretical analysis, we uncover
the limitations of existing overparameterization methods and show how the
proposed method alleviates them. 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 (1080p to 8K) SISR. Towards this, we estimate
hardware performance numbers for a commercial Arm 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 (e.g., 6x-8x higher FPS) than existing models on
mobile-NPU. Finally, SESR outperforms prior models by 1.5x-2x in latency on Arm
CPU and GPU when deployed on a real mobile device. The code for this work is
available at https://github.com/ARM-software/sesr.
