Residual Feature Distillation Network for Lightweight Image Super-Resolution

Jie Liu; Jie Tang; Gangshan Wu

doi:10.1007/978-3-030-67070-2_2

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Residual Feature Distillation Network for Lightweight Image Super-Resolution

Jie Liu Jie Tang Gangshan Wu

Year: 2020 Lecture notes in computer science Pages: 41-55 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-67070-2_2

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Keywords:

Computer science Residual Block (permutation group theory) Convolutional neural network Feature (linguistics) Discriminative model Distillation Enhanced Data Rates for GSM Evolution Artificial intelligence Code (set theory) Pattern recognition (psychology) Computer engineering Data mining Algorithm Mathematics

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454

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25.98

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1.00

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Topics

Advanced Image Processing Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image Processing Techniques and Applications

Physical Sciences → Engineering → Media Technology

Advanced Vision and Imaging

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Residual Feature Distillation Network for Lightweight Image Super-Resolution

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Citation History

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