  With the recently massive development in convolution neural networks,
numerous lightweight CNN-based image super-resolution methods have been
proposed for practical deployments on edge devices. However, most existing
methods focus on one specific aspect: network or loss design, which leads to
the difficulty of minimizing the model size. To address the issue, we conclude
block devising, architecture searching, and loss design to obtain a more
efficient SR structure. In this paper, we proposed an edge-enhanced feature
distillation network, named EFDN, to preserve the high-frequency information
under constrained resources. In detail, we build an edge-enhanced convolution
block based on the existing reparameterization methods. Meanwhile, we propose
edge-enhanced gradient loss to calibrate the reparameterized path training.
Experimental results show that our edge-enhanced strategies preserve the edge
and significantly improve the final restoration quality. Code is available at
https://github.com/icandle/EFDN.
