Edge-enhanced Feature Distillation Network for Efficient Super-Resolution. (arXiv:2204.08759v1 [cs.CV])

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 …

arxiv cv distillation edge network