NDLI: Video enhancement using a robust iterative SRR based on Andrew's Sine regularization technique

Content Provider	IEEE Xplore Digital Library
Author	Patanavijit, V.
Copyright Year	2009
Description	Author affiliation: Department of Computer and Network Engineering, Faculty of Engineering, Assumption University, Bangkok, Thailand (Patanavijit, V.)
Abstract	In this paper, we propose a alternative robust video enhancement algorithm using SRR based on the regularization ML technique. First, the classical registration process is used to estimate the relationship between the reference frame and other neighboring frames. Subsequently, the Andrew's Sine norm is used for measuring the difference between the projected estimate of the high quality image and each low high quality image and for removing outliers in the data. Moreover, Tikhonov regularization is incorporated in the proposed framework in order to remove artifacts from the final answer and to improve the rate of convergence. Later, the reconstructed video frame is estimated by minimize the total cost function. Finally, experimental results are presented to demonstrate the outstanding performance of the proposed algorithm in comparison to several previously published methods using standard sequences such as Foreman and Susie that are corrupted by several noise models such as AWGN, Poisson Noise, Salt & Pepper Noise and Speckle Noise.
Starting Page	115
Ending Page	118
File Size	1326366
Page Count	4
File Format	PDF
ISBN	9781424450152
DOI	10.1109/ISPACS.2009.5383890
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-01-07
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Additive white noise Gaussian noise Iterative algorithms Convergence Image reconstruction Cost function Standards publication AWGN Speckle
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

Video enhancement using a robust iterative SRR based on Hampel stochastic estimation

A robust iterative multiframe SRR based on Andrew's Sine stochastic estimation with Andrew;s Sine-Tikhonov regularization

Video Enhancement Using a Robust Iterative SRR Based on a Geman&McClure Stochastic Estimation

Video enhancement using a robust iterative SRR based on a Geman&McClure stochastic estimation with a general observation model

Video enhancement using a robust iterative SRR based on Leclerc stochastic estimation

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

Geman&McClure Stochastic Estimation for a Robust Iterative Multiframe SRR with Geman&McClure-Tikhonov Regularization

A Robust Iterative Multiframe Super-Resolution Reconstruction using a Huber Statistical Estimation Technique

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

Video enhancement using a robust iterative SRR based on Andrew's Sine regularization technique

Similar Documents

Video enhancement using a robust iterative SRR based on Hampel stochastic estimation

A robust iterative multiframe SRR based on Andrew's Sine stochastic estimation with Andrew;s Sine-Tikhonov regularization

Video Enhancement Using a Robust Iterative SRR Based on a Geman&McClure Stochastic Estimation

Video enhancement using a robust iterative SRR based on a Geman&McClure stochastic estimation with a general observation model

Video enhancement using a robust iterative SRR based on Leclerc stochastic estimation

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

Geman&McClure Stochastic Estimation for a Robust Iterative Multiframe SRR with Geman&McClure-Tikhonov Regularization

A Robust Iterative Multiframe Super-Resolution Reconstruction using a Huber Statistical Estimation Technique

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

Video enhancement using a robust iterative SRR based on Andrew's Sine regularization technique