Memory‐ and time‐efficient dense network for single‐image super‐resolution

Memory‐ and time‐efficient dense network for single‐image super‐resolution

Abstract Dense connections in convolutional neural networks (CNNs), which connect each layer to every other layer, can compensate for mid/high‐frequency information loss and further enhance high‐frequency signals. However, dense CNNs suffer from high memory usage due to the accumulation of concatena...

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Bibliographic Details
Main Authors: Nasrin Imanpour, Ahmad R. Naghsh‐Nilchi, Amirhassan Monadjemi, Hossein Karshenas, Kamal Nasrollahi, Thomas B. Moeslund
Format: Article
Language:English
Published: Wiley 2021-04-01
Series:IET Signal Processing
Subjects:
feature extraction
filtering theory
image resolution
convolutional neural nets
Online Access:https://doi.org/10.1049/sil2.12020
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https://doi.org/10.1049/sil2.12020

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