NDLI: Deep residual refining based pseudo-multi-frame network for effective single image super-resolution

Content Provider	IET Digital Library
Author	Mei, Kangfu Jiang, Aiwen Li, Juncheng Liu, Bo Ye, Jihua Wang, Mingwen
Abstract	Single image super-resolution (SISR) has gained great attraction and progress in recent years. Since the SISR is an ill-posed inverse problem, most researchers are concentrated on making efforts to learn effective and reasonable mapping functions from low-resolution observation to its potential high-resolution (HR) counterpart. In this study, the authors have proposed a deep residual refining based pseudo-multi-frame network for efficient SISR. A channel-wise attention mechanism is employed for residual refinement. It can ease residual learning process through explicitly modelling non-linear dependencies between channels by using global information embedding. Multiple potential HRs from different deconvolutional layers are further artificially learned, and then adaptively fused into final desired HR image. The authors call this strategy as pseudo-multi-frame SR. It could make full use of available redundant information possessed in hierarchical layers. They have evaluated the proposed network on several popular benchmark datasets. The experimental results have shown that the two highlights proposed can consistently boost final performance. The proposed network can outperform most of the state-of-the-art methods with acceptable less parameters.
Starting Page	591
Ending Page	599
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 4, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6057
Journal	IET Image Processing
Publisher Date	2018-12-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Channel-wise Attention Mechanism Computer Vision And Image Processing Technique Convolution Deconvolutional Layer Deep Residual Refining Global Information Embedding Image Resolution Inverse Problem Knowledge Engineering Technique Learning in AI Mathematical Analysis Optical, Image And Video Signal Processing Pseudomultiframe Network Residual Learning Process Single Image Super-resolution SISR
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Accurate single image super-resolution using cascading dense connections

Dual branches network for image super-resolution

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

Image super-resolution using conditional generative adversarial network

Survey of single image super-resolution reconstruction

ResDNN: deep residual learning for natural image denoising

Deep Residual Network Based on Image Priors for Single Image Super Resolution in FFA Images

Single-frame super-resolution for remote sensing images based on improved deep recursive residual network

Patch orientation-specified network for learning-based image super-resolution

Deep residual refining based pseudo-multi-frame network for effective single image super-resolution

Similar Documents

Accurate single image super-resolution using cascading dense connections

Dual branches network for image super-resolution

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

Image super-resolution using conditional generative adversarial network

Survey of single image super-resolution reconstruction

ResDNN: deep residual learning for natural image denoising

Deep Residual Network Based on Image Priors for Single Image Super Resolution in FFA Images

Single-frame super-resolution for remote sensing images based on improved deep recursive residual network

Patch orientation-specified network for learning-based image super-resolution

Deep residual refining based pseudo-multi-frame network for effective single image super-resolution