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Compnet: A New Scheme for Single Image Super Resolution Based on Deep Convolutional Neural Network


Compnet: A New Scheme for Single Image Super Resolution Based on Deep Convolutional Neural Network

Esmaeilzehi, Alireza, Ahmad, M. Omair ORCID: https://orcid.org/0000-0002-2924-6659 and Swamy, M.N.S. (2018) Compnet: A New Scheme for Single Image Super Resolution Based on Deep Convolutional Neural Network. IEEE Access, 6 . pp. 59963-59974. ISSN 2169-3536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2018.2874442


The features produced by the layers of a neural network become increasingly more sparse as the network gets deeper and consequently, the learning capability of the network is not further enhanced as the number of layers is increased. In this paper, a novel residual deep network, called CompNet, is proposed for the single image super resolution problem without an excessive increase in the network complexity. The idea behind the proposed network is to compose the residual signal that is more representative of the features produced by the different layers of the network and it is not as sparse. The proposed network is experimented on different benchmark datasets and is shown to outperform the state-of-the-art schemes designed to solve the super resolution problem.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Article
Authors:Esmaeilzehi, Alireza and Ahmad, M. Omair and Swamy, M.N.S.
Journal or Publication:IEEE Access
  • Concordia Open Access Author Fund
  • Natural Sciences and Engineering Research Council of Canada
  • Regroupment Strategique en Microelectronique du Quebec
Digital Object Identifier (DOI):10.1109/ACCESS.2018.2874442
Keywords:Image super resolution, residual learning, deep learning
ID Code:984719
Deposited On:30 Nov 2018 20:17
Last Modified:30 Nov 2018 20:17


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