Open AccessAccurate single image super‐resolution using cascading dense connections
Author(s) -
Wei Wei,
Feng Guoqi,
Zhang Qi,
Cui Dongliang,
Zhang Miao,
Chen Feng
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2019.0392
Subject(s) - computer science , convolutional neural network , metric (unit) , artificial intelligence , pattern recognition (psychology) , deep learning , construct (python library) , layer (electronics) , image (mathematics) , algorithm , computer network , operations management , chemistry , organic chemistry , economics
In recent years, deep learning methods, especially deep convolutional neural network, have been successfully applied to the single‐image super‐resolution (SISR) task. In this Letter, the authors propose an accurate SISR method by introducing cascading dense connections in a very deep network. In detail, they construct the cascading dense network (CDN) to fully make use of the hierarchical features from all the convolutional layers, which implements a cascading mechanism upon the dense connected convolutional layers. The cascading dense connection in the CDN enables short and long paths to be built directly from the output to each layer, alleviating the vanishing‐gradient problem of very deep networks. Extensive experiments show that CDN achieves state‐of‐the‐art performance on traditional metrics (PSNR and SSIM). Also, they introduce object recognition as the additional evaluation metric for SISR, which further demonstrates the effectiveness of the authors’ method.