NDLI: Speedy and accurate image super-resolution via deeply recursive CNN with skip connection and network in network

Content Provider	IET Digital Library
Author	Guo, Dan Niu, Yanxiong Xie, Pengyan
Abstract	The single image super-resolution (SISR) methods based on the deep convolutional neural network (CNN) have recently achieved significant improvements in accuracy, advancing the state of the art. However, these deeper models are computationally expensive and require a large number of parameters. Accordingly, they demand more memory and are unsuitable for on-chip devices. In this study, a novel SISR method using a deeply recursive CNN with skip connections and a network in network structure is proposed. The deeply recursive CNN with skip connections is adopted for the image feature extraction at both local and global levels. Parallelised 1 × 1 CNNs, usually called a network in network structure, are adopted for image reconstruction. Specifically, recursive learning is utilised to control the number of model parameters needed and residual learning is used to ease the difficulty of training. The proposed method performs favourably against the state-of-the-art methods in terms of computational speed and accuracy. It significantly outperforms the previous methods by a large margin, while demanding far fewer parameters. This model requires less memory and is friendly to on-chip devices.
Starting Page	1201
Ending Page	1209
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 7, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5907
Journal	IET Image Processing
Publisher Date	2019-03-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Accurate Image Super-resolution Computer Vision And Image Processing Technique Convolution Deep Convolutional Neural Network Deeply Recursive CNN Feature Extraction Image Feature Extraction Image Reconstruction Image Resolution Knowledge Engineering Technique Learning in AI Network Structure Neural Computing Technique Neural Nets Novel SISR Method On-chip Device Optical, Image And Video Signal Processing Parallelised 1 × 1 CNNs Recursive Learning Single Image Super-resolution Method Skip Connection State-of-the-art Method
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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