An efficient algorithm for superresolution in medium field imaging

Andy C. Yau; N. K. Bose; Michael K. Ng

doi:10.1007/s11045-007-0020-5

An efficient algorithm for superresolution in medium field imaging

Andy C. Yau^*, N. K. Bose, Michael K. Ng

^*Corresponding author for this work

Department of Mathematics

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

4 Citations (Scopus)

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Abstract

In this paper, we study the problem of reconstruction of a high-resolution (HR) image from several blurred low-resolution (LR) image frames in medium field. The image frames consist of blurred, decimated, and noisy versions of a HR image. The HR image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, a HR image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore HR images in the aperiodic boundary condition. Computer simulations are given to illustrate the effectiveness of the proposed approach.

Original language	English
Pages (from-to)	173-188
Number of pages	16
Journal	Multidimensional Systems and Signal Processing
Volume	18
Issue number	2-3
DOIs	https://doi.org/10.1007/s11045-007-0020-5
Publication status	Published - Sept 2007

Scopus Subject Areas

Software
Signal Processing
Information Systems
Hardware and Architecture
Computer Science Applications
Artificial Intelligence
Applied Mathematics

User-Defined Keywords

Superresolution
Medium field
Preconditioned conjugate gradient method
Fast Fourier transforms
Toeplitz matrix
Deblussing

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10.1007/s11045-007-0020-5

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Cite this

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title = "An efficient algorithm for superresolution in medium field imaging",

abstract = "In this paper, we study the problem of reconstruction of a high-resolution (HR) image from several blurred low-resolution (LR) image frames in medium field. The image frames consist of blurred, decimated, and noisy versions of a HR image. The HR image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, a HR image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore HR images in the aperiodic boundary condition. Computer simulations are given to illustrate the effectiveness of the proposed approach.",

keywords = "Superresolution, Medium field, Preconditioned conjugate gradient method, Fast Fourier transforms, Toeplitz matrix, Deblussing",

author = "Yau, {Andy C.} and Bose, {N. K.} and Ng, {Michael K.}",

note = "Funding Information: This research was conducted with support from the Army Research Office Grant DAAD 19-03-1-0261 and the National Science Foundation Grant CCF-0429481. Research supported in part by RGC Grant Nos. 7130/02P, 7046/03P, 7035/04P and 7035/04P and FRG/04-05/II-51.",

year = "2007",

month = sep,

doi = "10.1007/s11045-007-0020-5",

language = "English",

volume = "18",

pages = "173--188",

journal = "Multidimensional Systems and Signal Processing",

issn = "0923-6082",

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T1 - An efficient algorithm for superresolution in medium field imaging

AU - Yau, Andy C.

AU - Bose, N. K.

AU - Ng, Michael K.

N1 - Funding Information: This research was conducted with support from the Army Research Office Grant DAAD 19-03-1-0261 and the National Science Foundation Grant CCF-0429481. Research supported in part by RGC Grant Nos. 7130/02P, 7046/03P, 7035/04P and 7035/04P and FRG/04-05/II-51.

PY - 2007/9

Y1 - 2007/9

N2 - In this paper, we study the problem of reconstruction of a high-resolution (HR) image from several blurred low-resolution (LR) image frames in medium field. The image frames consist of blurred, decimated, and noisy versions of a HR image. The HR image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, a HR image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore HR images in the aperiodic boundary condition. Computer simulations are given to illustrate the effectiveness of the proposed approach.

AB - In this paper, we study the problem of reconstruction of a high-resolution (HR) image from several blurred low-resolution (LR) image frames in medium field. The image frames consist of blurred, decimated, and noisy versions of a HR image. The HR image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, a HR image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore HR images in the aperiodic boundary condition. Computer simulations are given to illustrate the effectiveness of the proposed approach.

KW - Superresolution

KW - Medium field

KW - Preconditioned conjugate gradient method

KW - Fast Fourier transforms

KW - Toeplitz matrix

KW - Deblussing

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U2 - 10.1007/s11045-007-0020-5

DO - 10.1007/s11045-007-0020-5

M3 - Journal article

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SN - 0923-6082

VL - 18

SP - 173

EP - 188

JO - Multidimensional Systems and Signal Processing

JF - Multidimensional Systems and Signal Processing

IS - 2-3

ER -

An efficient algorithm for superresolution in medium field imaging

Abstract

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