Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search

Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by using much deeper layers, which are not friendly to constrained resources. Pursuing a trade-off between the restoration cap...

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Bibliographic Details
Published in2020 25th International Conference on Pattern Recognition (ICPR) pp. 59 - 64
Main Authors Chu, Xiangxiang, Zhang, Bo, Ma, Hailong, Xu, Ruijun, Li, Qingyuan
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.01.2021
Subjects
Computer architecture
Image coding
Performance evaluation
PSNR
Reinforcement learning
Superresolution
Training
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Summary:Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by using much deeper layers, which are not friendly to constrained resources. Pursuing a trade-off between the restoration capacity and the simplicity of models is still non-trivial. Recent contributions are struggling to manually maximize this balance, while our work achieves the same goal automatically with neural architecture search. Specifically, we handle super-resolution with a multi-objective approach. We also propose an elastic search tactic at both micro and macro level, based on a hybrid controller that profits from evolutionary computation and reinforcement learning. Quantitative experiments help us to draw a conclusion that our generated models dominate most of the state-of-the-art methods with respect to the individual FLOPS.
DOI:10.1109/ICPR48806.2021.9413080