Paper Info
Title
Compnet: A New Scheme for Single Image Super Resolution Based on Deep Convolutional Neural Network.
Abstract
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.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2874442
IEEE ACCESS
Keywords
Field
DocType
Image super resolution,residual learning,deep learning
Iterative reconstruction,Residual,Network complexity,Pattern recognition,Computer science,Convolutional neural network,Feature extraction,Artificial intelligence,Artificial neural network,Superresolution,Image resolution,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Alireza Esmaeilzehi113.73
M. O. Ahmad21157154.87
M. N. S. Swamy31037135.52