High-resolution image reconstruction of low-resolution DCT-based compressed images

Sung Cheol Park, Moon Gi Kang, C. Andrew Segall, Aggelos K. Katsaggelos

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The problem of recovering a high-resolution image from a sequence of low-resolution DCT-based compressed images is considered in this paper. The presence of the compression system complicates the recovery problem, as the operation reduces the amount of frequency aliasing in the low-resolution frames and introduces a non-linear quantization process. The effect of the quantization error and resulting inaccurate sub-pixel motion information is modeled as a zero-mean additive correlated Gaussian noise. A regularization functional is introduced not only to reflect the relative amount of registration error in each low-resolution image but also to determine the regularization parameter without any prior knowledge in the reconstruction procedure. The effectiveness of the proposed algorithm is demonstrated experimentally.

Original languageEnglish
Pages (from-to)1665-1668
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
DOIs
Publication statusPublished - 2002 Jan 1

Fingerprint

Image resolution
Image reconstruction
Optical resolving power
Pixels
Recovery

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

@article{76642b5f3b6b4e29a5d994326e08a01e,
title = "High-resolution image reconstruction of low-resolution DCT-based compressed images",
abstract = "The problem of recovering a high-resolution image from a sequence of low-resolution DCT-based compressed images is considered in this paper. The presence of the compression system complicates the recovery problem, as the operation reduces the amount of frequency aliasing in the low-resolution frames and introduces a non-linear quantization process. The effect of the quantization error and resulting inaccurate sub-pixel motion information is modeled as a zero-mean additive correlated Gaussian noise. A regularization functional is introduced not only to reflect the relative amount of registration error in each low-resolution image but also to determine the regularization parameter without any prior knowledge in the reconstruction procedure. The effectiveness of the proposed algorithm is demonstrated experimentally.",
author = "Park, {Sung Cheol} and Kang, {Moon Gi} and Segall, {C. Andrew} and Katsaggelos, {Aggelos K.}",
year = "2002",
month = "1",
day = "1",
doi = "10.1109/ICASSP.2002.5744939",
language = "English",
volume = "2",
pages = "1665--1668",
journal = "Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",
issn = "0736-7791",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

High-resolution image reconstruction of low-resolution DCT-based compressed images. / Park, Sung Cheol; Kang, Moon Gi; Segall, C. Andrew; Katsaggelos, Aggelos K.

In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Vol. 2, 01.01.2002, p. 1665-1668.

Research output: Contribution to journalArticle

TY - JOUR

T1 - High-resolution image reconstruction of low-resolution DCT-based compressed images

AU - Park, Sung Cheol

AU - Kang, Moon Gi

AU - Segall, C. Andrew

AU - Katsaggelos, Aggelos K.

PY - 2002/1/1

Y1 - 2002/1/1

N2 - The problem of recovering a high-resolution image from a sequence of low-resolution DCT-based compressed images is considered in this paper. The presence of the compression system complicates the recovery problem, as the operation reduces the amount of frequency aliasing in the low-resolution frames and introduces a non-linear quantization process. The effect of the quantization error and resulting inaccurate sub-pixel motion information is modeled as a zero-mean additive correlated Gaussian noise. A regularization functional is introduced not only to reflect the relative amount of registration error in each low-resolution image but also to determine the regularization parameter without any prior knowledge in the reconstruction procedure. The effectiveness of the proposed algorithm is demonstrated experimentally.

AB - The problem of recovering a high-resolution image from a sequence of low-resolution DCT-based compressed images is considered in this paper. The presence of the compression system complicates the recovery problem, as the operation reduces the amount of frequency aliasing in the low-resolution frames and introduces a non-linear quantization process. The effect of the quantization error and resulting inaccurate sub-pixel motion information is modeled as a zero-mean additive correlated Gaussian noise. A regularization functional is introduced not only to reflect the relative amount of registration error in each low-resolution image but also to determine the regularization parameter without any prior knowledge in the reconstruction procedure. The effectiveness of the proposed algorithm is demonstrated experimentally.

UR - http://www.scopus.com/inward/record.url?scp=0036296227&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036296227&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2002.5744939

DO - 10.1109/ICASSP.2002.5744939

M3 - Article

VL - 2

SP - 1665

EP - 1668

JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

SN - 0736-7791

ER -