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

Content Provider	IET Digital Library
Author	Yoo, S. B. Han, M.
Abstract	Learning-based image super-resolution is considered as a promising solution to reconstruct a high-resolution image from a low-resolution image. To improve the super-resolution performance dramatically, this Letter focuses on the effect of training dataset on the performance and proposes an image super-resolution scheme based on patch orientation-specified network. In particular, a deep neural network is trained using patches with a specific orientation and angular transformation is combined with the neural network to cope with various orientations in input patches. Experimental results show the suggested network model is superior to existing state-of-the-art super-resolution alternatives.
Starting Page	1233
Ending Page	1235
Page Count	3
ISSN	00135194
Volume Number	55
e-ISSN	1350911X
Issue Number	Issue 23, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/55/23
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.1219
Journal	Electronics Letters
Publisher Date	2019-09-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Deep Neural Network Existing State-of-the-art Super-resolution Alternatives High-resolution Image Image Resolution Image Super-resolution Scheme Interpolation And Function Approximation Knowledge Engineering Technique Learning in AI Learning-based Image Super-resolution Low-resolution Image Neural Nets Numerical Analysis Optical, Image And Video Signal Processing Patch Orientation-specified Network Statistics Super-resolution Performance
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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

Survey of single image super-resolution reconstruction

Multimodal deep network learning-based image annotation

Accurate single image super-resolution using cascading dense connections

Generative adversarial image super-resolution network for multiple degradations

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

DeepInterpolation: fusion of multiple interpolations and CNN to obtain super-resolution

Deep-learning-based depth estimation from light field images

Guided filter-based multi-scale super-resolution reconstruction

Deep-network based method for joint image deblocking and super-resolution

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

Similar Documents

Survey of single image super-resolution reconstruction

Multimodal deep network learning-based image annotation

Accurate single image super-resolution using cascading dense connections

Generative adversarial image super-resolution network for multiple degradations

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

DeepInterpolation: fusion of multiple interpolations and CNN to obtain super-resolution

Deep-learning-based depth estimation from light field images

Guided filter-based multi-scale super-resolution reconstruction

Deep-network based method for joint image deblocking and super-resolution

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