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A frequency domain neural network for fast image super-resolution
conference contribution
posted on 2018-01-01, 00:00 authored by Junxuan Li, Shaodi You, Antonio Robles-KellyAntonio Robles-KellyIn this paper, we present a frequency domain neural network for image super-resolution. The network employs the convolution theorem so as to cast convolutions in the spatial domain as products in the frequency domain. Moreover, the non-linearity in deep nets, of ten achieved by a rectifier unit, is here cast as a convolution in the frequency domain. This not only yields a network which is very computationally efficient at testing, but also one whose parameters can all be learnt accordingly. The network can be trained using back propagation and is devoid of complex numbers due to the use of the Hartley transform as an alternative to the Fourier transform. Moreover, the network is potentially applicable to other problems elsewhere in computer vision and image processing which are of ten cast in the frequency domain. We show results on super-resolution and compare against alternatives elsewhere in the literature. In our experiments, our network is one to two orders of magnitude faster than the alternatives with a marginal loss of performance.
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Event
Neural Networks. Joint Conference (2018 : Rio de Janeiro, Brazil)Pagination
1 - 8Publisher
IEEELocation
Rio de Janeiro, BrazilPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2018-07-08End date
2018-07-13ISSN
2161-4407ISBN-13
9781509060146Language
engPublication classification
E1.1 Full written paper - refereedTitle of proceedings
IJCNN 2018 : Proceedings of the International Joint Conference on Neural NetworksUsage metrics
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