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
this https URL

本文提出通过结合 block devising、architecture searching 和 loss design，构建轻量级卷积神经网络（CNN）来提高图像超分辨率质量。我们提出了一种名为 EFDN 的边缘增强特征蒸馏网络，在受限资源下保留高频信息。该方法构建了基于现有重参数方法的边缘增强卷积模块，并提出了边缘增强梯度损失来校准重参数路径训练。实验结果表明，提出的边缘增强策略能够保留边缘并显著提高最终恢复质量。