NDLI: Single Frame Super-Resolution: A New Learning Based Approach and Use of IGMRF Prior

Content Provider	IEEE Xplore Digital Library
Author	Gajjar, P.P. Joshi, M.V.
Copyright Year	2008
Description	Author affiliation: Dhirubhai Ambani - Inst. of Inf. & Commun. Technol., Gandhinagar (Gajjar, P.P.; Joshi, M.V.)
Abstract	In this paper, we propose a new learning based approach for super-resolving an image captured at low spatial resolution. Given the low spatial resolution test image and a training set consisting of low and high spatial resolution images, all captured using the same camera, we obtain super-resolution for the test image. We propose a new wavelet based learning technique that learns the high frequency details for the test image from the training set and thus obtain an initial high resolution estimate. Since super-resolution is an ill-posed problem we solve it using regularization framework. We model the low resolution image as the aliased and noisy version of the corresponding high resolution image and estimate the aliasing matrix using the test image and the initial high resolution (HR) estimate. The super-resolved image is modeled as an inhomogeneous Gaussian Markov Random Field (IGMRF) and the IGMRF prior model parameters are estimated using the initial HR estimate. Finally, the cost function formed is minimized using simple gradient descent approach. We demonstrate the effectiveness of the proposed approach by conducting experiments on gray scale as well as on color images. The method is compared with another existing learning-based approach which uses training set consisting of HR images only and employs autoregressive (AR) and wavelet priors. The advantage of the our approach when compared to motion-based methods is that there is no need of multiple observations and also registration. The proposed approach can be used in applications such as wildlife sensor network where memory, transmission bandwidth and camera cost are main constraints.
Starting Page	636
Ending Page	643
File Size	492537
Page Count	8
File Format	PDF
ISBN	9780769534763
DOI	10.1109/ICVGIP.2008.21
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-16
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image resolution Parameter estimation Wildlife Color Cameras Cost function Frequency estimation Spatial resolution Testing Markov random fields
Content Type	Text
Resource Type	Article

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

New Learning Based Super-Resolution: Use of DWT and IGMRF Prior

Adaptive Markov random fields for example-based super-resolution of faces

Adaptive Markov random fields for example-based super-resolution of faces

MAP Estimation for Multiresolution Fusion in Remotely Sensed Images Using an IGMRF Prior Model

Multiresolution Fusion in Remotely Sensed Images Using an IGMRF Prior and Map Estimation

Spatial-Depth Super Resolution for Range Images

A learning-based method for image super-resolution from zoomed observations

PSF Recovery from Examples for Blind Super-Resolution

Map-Based Single-Frame Super-Resolution Image Reconstruction for License Plate Recognition

Single Frame Super-Resolution: A New Learning Based Approach and Use of IGMRF Prior

Similar Documents

New Learning Based Super-Resolution: Use of DWT and IGMRF Prior

Adaptive Markov random fields for example-based super-resolution of faces

Adaptive Markov random fields for example-based super-resolution of faces

MAP Estimation for Multiresolution Fusion in Remotely Sensed Images Using an IGMRF Prior Model

Multiresolution Fusion in Remotely Sensed Images Using an IGMRF Prior and Map Estimation

Spatial-Depth Super Resolution for Range Images

A learning-based method for image super-resolution from zoomed observations

PSF Recovery from Examples for Blind Super-Resolution

Map-Based Single-Frame Super-Resolution Image Reconstruction for License Plate Recognition

Single Frame Super-Resolution: A New Learning Based Approach and Use of IGMRF Prior