Edge-enhanced Feature Distillation Network for Efficient Super-Resolution

• URL: http://arxiv.org/abs/2204.08759v1
• Date: Tue, 19 Apr 2022 08:57:35 GMT
• Title: Edge-enhanced Feature Distillation Network for Efficient Super-Resolution
• Abstract summary: We propose an edge-enhanced feature distillation network, named EFDN, to preserve the high-frequency information under constrained resources. We show that our edge-enhanced strategies preserve the edge and significantly improve the final restoration quality.
• Score: 6.245537312562826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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