Open AccessDual branches network for image super‐resolution
Author(s) -
Matsune Ai,
Cheng Guoan,
Zhan Shu
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.7562
Subject(s) - convolutional neural network , dual (grammatical number) , computer science , residual , artificial intelligence , path (computing) , key (lock) , pattern recognition (psychology) , image (mathematics) , deep learning , computer vision , algorithm , art , literature , computer security , programming language
Recent studies have shown that deep convolutional neural networks (CNNs) significantly boosted the performance of single‐image super‐resolution (SISR). In this Letter, the authors present a novel dual branches network (DBN) for SISR. Different from traditional CNN, the authors' DBN utilises the benefits of the residual structure and the densely connected structure together. Their key strategy is to divide the input path of the network into dual branches: a residual branch and a dense branch. This dual branches structure reuses valuable features and explores new features effectively. Experimental comparisons demonstrated the high ability of their DBN over the state‐of‐the‐art framework for SISR with alleviating blurs of output images.