NDLI: MRI Iterative Super Resolution with Wiener Filter Regularization

Content Provider	IEEE Xplore Digital Library
Author	Aguena, M.L.S. Mascarenha, N.D.A. Anacleto, J.C. Fels, S.S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of British Columbia, Vancouver, BC, Canada (Fels, S.S.) \|\| Dept. de Comput., UFSCar - Univ. Fed. de Sao Carlos, Sao Carlos, Brazil (Aguena, M.L.S.; Mascarenha, N.D.A.; Anacleto, J.C.)
Abstract	The swallowing process affects several aspects of one's welfare, as nutrition, hydration, respiration and hearing. Magnetic resonance imaging (MRI) has been a valuable tool to study swallowing, since it is a non-invasive procedure that can dynamically capture the shapes of the tongue and other elements involved in the process. The resolution enhancement of the MRI frames support directly diseases diagnoses, helping the visual analysis, or can be a pre-processing tool to segmentation, classification, recognition or modelling. MRI frames with better resolution, with less blurring or noise can be obtained changing the acquisition process, or using more powerful devices, but the cost of this solution is higher than applying computational Super-Resolution (SR) techniques. This paper studies a Bayesian approach to provide a Wiener filter to regularize the conjugate gradient solution, and promote an adaptation of an iterative SR method for non-rigid registration that can be generalized to other iterative SR methods.
Starting Page	155
Ending Page	162
File Size	1151163
Page Count	8
File Format	PDF
ISBN	9780769550992
ISSN	15301834
DOI	10.1109/SIBGRAPI.2013.30
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-05
Publisher Place	Peru
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic resonance imaging Noise Image resolution Iterative methods Interpolation Correlation Vectors Bayesian Regularization Super-Resolution Swallowing MRI Wiener Filter Conjugate Gradient
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

Single image super-resolution using iterative Wiener filter

Iterative image reconstruction in MRI with separate magnitude and phase regularization

A Robust Iterative Multiframe Super-Resolution Reconstruction using a Huber Bayesian Approach with Huber-Tikhonov Regularization

A supervised patch-based image reconstruction technique: Application to brain MRI super-resolution

Gradient-based adaptive interpolation in super-resolution image restoration

A Robust Iterative Multiframe Super-Resolution Reconstruction using a Bayesian Approach with Lorentzian Norm

A Lorentzian Bayesian Approach for Robust Iterative Multiframe Super-Resolution Reconstruction with Lorentzian-Tikhonov Regularization

Edge-preserving MRI Super Resolution using a high frequency regularization technique

Improved wiener filter super-resolution algorithm for passive millimeter wave imaging

MRI Iterative Super Resolution with Wiener Filter Regularization

Similar Documents

Single image super-resolution using iterative Wiener filter

Iterative image reconstruction in MRI with separate magnitude and phase regularization

A Robust Iterative Multiframe Super-Resolution Reconstruction using a Huber Bayesian Approach with Huber-Tikhonov Regularization

A supervised patch-based image reconstruction technique: Application to brain MRI super-resolution

Gradient-based adaptive interpolation in super-resolution image restoration

A Robust Iterative Multiframe Super-Resolution Reconstruction using a Bayesian Approach with Lorentzian Norm

A Lorentzian Bayesian Approach for Robust Iterative Multiframe Super-Resolution Reconstruction with Lorentzian-Tikhonov Regularization

Edge-preserving MRI Super Resolution using a high frequency regularization technique

Improved wiener filter super-resolution algorithm for passive millimeter wave imaging

MRI Iterative Super Resolution with Wiener Filter Regularization