Superresolution Image Reconstruction from Blurred Observations by Multisensors

Wai Ki Ching; Michael K. Ng; Kenton N. Sze; Andy C. Yau

doi:10.1002/ima.10053

Superresolution Image Reconstruction from Blurred Observations by Multisensors

Wai Ki Ching, Michael K. Ng^*, Kenton N. Sze, Andy C. Yau

^*Corresponding author for this work

Department of Mathematics

Research output: Contribution to journal › Journal article › peer-review

3 Citations (Scopus)

Abstract

Superresolution image reconstruction refers to obtaining an image at a resolution higher than that of the camera (sensor) used in recording the image. In this article, we present a joint minimization model with an objective function setup that comprises three terms: the data-fitting term (DFT), the regularization term for the reconstructed image, and the observed low-resolution images. An alternating minimization iterative algorithm is presented to reconstruct the image. We also analyze the alternating minimization iterative algorithm and show that it converges globally for H ¹-norm or total-variation regularization that are functional for the reconstructed image. Numeric examples are given to illustrate the effectiveness of the joint minimization model and the efficiency of the algorithm.

Original language	English
Pages (from-to)	153-160
Number of pages	8
Journal	International Journal of Imaging Systems and Technology
Volume	13
Issue number	3
DOIs	https://doi.org/10.1002/ima.10053
Publication status	Published - 30 Sept 2003

Scopus Subject Areas

Electronic, Optical and Magnetic Materials
Software
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1002/ima.10053

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title = "Superresolution Image Reconstruction from Blurred Observations by Multisensors",

abstract = "Superresolution image reconstruction refers to obtaining an image at a resolution higher than that of the camera (sensor) used in recording the image. In this article, we present a joint minimization model with an objective function setup that comprises three terms: the data-fitting term (DFT), the regularization term for the reconstructed image, and the observed low-resolution images. An alternating minimization iterative algorithm is presented to reconstruct the image. We also analyze the alternating minimization iterative algorithm and show that it converges globally for H 1-norm or total-variation regularization that are functional for the reconstructed image. Numeric examples are given to illustrate the effectiveness of the joint minimization model and the efficiency of the algorithm.",

author = "Ching, {Wai Ki} and Ng, {Michael K.} and Sze, {Kenton N.} and Yau, {Andy C.}",

note = "Research Grants Council. Grant Numbers: HKU 7130/02P, 7046/03P HKU CRCG. Grant Numbers: 10203907, 10203501, 1020443 Publisher Copyright: {\textcopyright} 2003 Wiley Periodicals, Inc.",

year = "2003",

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doi = "10.1002/ima.10053",

language = "English",

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TY - JOUR

T1 - Superresolution Image Reconstruction from Blurred Observations by Multisensors

AU - Ching, Wai Ki

AU - Ng, Michael K.

AU - Sze, Kenton N.

AU - Yau, Andy C.

PY - 2003/9/30

Y1 - 2003/9/30

N2 - Superresolution image reconstruction refers to obtaining an image at a resolution higher than that of the camera (sensor) used in recording the image. In this article, we present a joint minimization model with an objective function setup that comprises three terms: the data-fitting term (DFT), the regularization term for the reconstructed image, and the observed low-resolution images. An alternating minimization iterative algorithm is presented to reconstruct the image. We also analyze the alternating minimization iterative algorithm and show that it converges globally for H 1-norm or total-variation regularization that are functional for the reconstructed image. Numeric examples are given to illustrate the effectiveness of the joint minimization model and the efficiency of the algorithm.

AB - Superresolution image reconstruction refers to obtaining an image at a resolution higher than that of the camera (sensor) used in recording the image. In this article, we present a joint minimization model with an objective function setup that comprises three terms: the data-fitting term (DFT), the regularization term for the reconstructed image, and the observed low-resolution images. An alternating minimization iterative algorithm is presented to reconstruct the image. We also analyze the alternating minimization iterative algorithm and show that it converges globally for H 1-norm or total-variation regularization that are functional for the reconstructed image. Numeric examples are given to illustrate the effectiveness of the joint minimization model and the efficiency of the algorithm.

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DO - 10.1002/ima.10053

M3 - Journal article

AN - SCOPUS:0242272600

SN - 0899-9457

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EP - 160

JO - International Journal of Imaging Systems and Technology

JF - International Journal of Imaging Systems and Technology

IS - 3

ER -

Superresolution Image Reconstruction from Blurred Observations by Multisensors

Abstract

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