Abstract
Image denoising is a fundamental but critical task. Previous works based on deep networks have made great progress, but suffer from the problem of computational overload. This paper addresses the demands by (1) a lightweight denoising network and (2) a novel knowledge distillation algorithm. The experimental results show the usefulness of the RS-KD on the proposed lightweight network and consistent gains that can be obtained on both synthetic and real-world datasets. Especially, benefiting from the retargeting supervision, our proposed distillation framework allows for arbitrary high-performance teacher networks.
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Acknowledgements
This work was supported by the National Science and Technology Major Project under Grant 2018AAA0102100, and the National Natural Science Foundation of China under Grant 61902435. We are grateful for resources from the High Performance Computing Center of Central South University.
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Zou, B., Zhang, Y., Wang, M., Liu, S. (2022). Toward Efficient Image Denoising: A Lightweight Network with Retargeting Supervision Driven Knowledge Distillation. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2022. Lecture Notes in Computer Science, vol 13443. Springer, Cham. https://doi.org/10.1007/978-3-031-23473-6_2
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