Regularized multichannel restoration approach for globally optimal high-resolution video sequence

Min Cheol Hong; Moon Gi Kang; Aggelos K Katsaggelos

doi:10.1117/12.263211

Regularized multichannel restoration approach for globally optimal high-resolution video sequence

Min Cheol Hong^*, Moon Gi Kang, Aggelos K Katsaggelos

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Contribution to journal › Conference article

30 Scopus citations

Abstract

This paper introduces an iterative regularized approach to obtain a high resolution video sequence. A multiple input smoothing convex functional is defined and used to obtain a globally optimal high resolution video sequence. A mathematical model of multiple inputs is described by using the point spread function between the original and bilinearly interpolated images in the spatial domain, and motion estimation between frames in the temporal domain. Properties of the proposed smoothing convex functional are analyzed. An iterative algorithm is utilized for obtaining a solution. The regularization parameter is updated at each iteration step from the partially restored video sequence. Experimental results demonstrate the capability of the proposed approach.

Original language	English (US)
Pages (from-to)	1306-1316
Number of pages	11
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	3024
DOIs	https://doi.org/10.1117/12.263211
State	Published - Dec 1 1997
Event	Visual Communications and Image Processing '97 - San Jose, CA, United States Duration: Feb 12 1997 → Feb 12 1997

Keywords

Iterative technique
Motion vector
Regularization parameter
Smoothing convex functional

ASJC Scopus subject areas

Applied Mathematics
Computer Science Applications
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials
Condensed Matter Physics

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title = "Regularized multichannel restoration approach for globally optimal high-resolution video sequence",

abstract = "This paper introduces an iterative regularized approach to obtain a high resolution video sequence. A multiple input smoothing convex functional is defined and used to obtain a globally optimal high resolution video sequence. A mathematical model of multiple inputs is described by using the point spread function between the original and bilinearly interpolated images in the spatial domain, and motion estimation between frames in the temporal domain. Properties of the proposed smoothing convex functional are analyzed. An iterative algorithm is utilized for obtaining a solution. The regularization parameter is updated at each iteration step from the partially restored video sequence. Experimental results demonstrate the capability of the proposed approach.",

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author = "Hong, {Min Cheol} and Kang, {Moon Gi} and Katsaggelos, {Aggelos K}",

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N2 - This paper introduces an iterative regularized approach to obtain a high resolution video sequence. A multiple input smoothing convex functional is defined and used to obtain a globally optimal high resolution video sequence. A mathematical model of multiple inputs is described by using the point spread function between the original and bilinearly interpolated images in the spatial domain, and motion estimation between frames in the temporal domain. Properties of the proposed smoothing convex functional are analyzed. An iterative algorithm is utilized for obtaining a solution. The regularization parameter is updated at each iteration step from the partially restored video sequence. Experimental results demonstrate the capability of the proposed approach.

AB - This paper introduces an iterative regularized approach to obtain a high resolution video sequence. A multiple input smoothing convex functional is defined and used to obtain a globally optimal high resolution video sequence. A mathematical model of multiple inputs is described by using the point spread function between the original and bilinearly interpolated images in the spatial domain, and motion estimation between frames in the temporal domain. Properties of the proposed smoothing convex functional are analyzed. An iterative algorithm is utilized for obtaining a solution. The regularization parameter is updated at each iteration step from the partially restored video sequence. Experimental results demonstrate the capability of the proposed approach.

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KW - Motion vector

KW - Regularization parameter

KW - Smoothing convex functional

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