IEICE TRANSACTIONS on Information
Mixed lp/l1 Norm Minimization Approach to Intra-Frame Super-Resolution
Summary :
This paper deals with the problem of reconstructing a high-resolution digital image from a single low-resolution digital image and proposes a new intra-frame super-resolution algorithm based on the mixed lp/l1 norm minimization. Introducing some assumptions, this paper formulates the super-resolution problem as a mixed l0/l1 norm minimization and relaxes the l0 norm term to the lp norm to avoid ill-posedness. A heuristic iterative algorithm is proposed based on the iterative reweighted least squares (IRLS). Numerical examples show that the proposed algorithm achieves super-resolution efficiently.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.10 pp.2814-2817
- Publication Date
- 2014/10/01
- Publicized
- 2014/07/22
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8086
- Type of Manuscript
- LETTER
- Category
- Image Processing and Video Processing
Authors
Kazuma SHIMADA
Tokyo University of Science
Katsumi KONISHI
Kogakuin University
Kazunori URUMA
Tokyo University of Science
Tomohiro TAKAHASHI
Tokyo University of Science
Toshihiro FURUKAWA
Tokyo University of Science
Keyword
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Kazuma SHIMADA, Katsumi KONISHI, Kazunori URUMA, Tomohiro TAKAHASHI, Toshihiro FURUKAWA, "Mixed lp/l1 Norm Minimization Approach to Intra-Frame Super-Resolution" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 10, pp. 2814-2817, October 2014, doi: 10.1587/transinf.2014EDL8086.
Abstract: This paper deals with the problem of reconstructing a high-resolution digital image from a single low-resolution digital image and proposes a new intra-frame super-resolution algorithm based on the mixed lp/l1 norm minimization. Introducing some assumptions, this paper formulates the super-resolution problem as a mixed l0/l1 norm minimization and relaxes the l0 norm term to the lp norm to avoid ill-posedness. A heuristic iterative algorithm is proposed based on the iterative reweighted least squares (IRLS). Numerical examples show that the proposed algorithm achieves super-resolution efficiently.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8086/_p
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@ARTICLE{e97-d_10_2814,
author={Kazuma SHIMADA, Katsumi KONISHI, Kazunori URUMA, Tomohiro TAKAHASHI, Toshihiro FURUKAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Mixed lp/l1 Norm Minimization Approach to Intra-Frame Super-Resolution},
year={2014},
volume={E97-D},
number={10},
pages={2814-2817},
abstract={This paper deals with the problem of reconstructing a high-resolution digital image from a single low-resolution digital image and proposes a new intra-frame super-resolution algorithm based on the mixed lp/l1 norm minimization. Introducing some assumptions, this paper formulates the super-resolution problem as a mixed l0/l1 norm minimization and relaxes the l0 norm term to the lp norm to avoid ill-posedness. A heuristic iterative algorithm is proposed based on the iterative reweighted least squares (IRLS). Numerical examples show that the proposed algorithm achieves super-resolution efficiently.},
keywords={},
doi={10.1587/transinf.2014EDL8086},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Mixed lp/l1 Norm Minimization Approach to Intra-Frame Super-Resolution
T2 - IEICE TRANSACTIONS on Information
SP - 2814
EP - 2817
AU - Kazuma SHIMADA
AU - Katsumi KONISHI
AU - Kazunori URUMA
AU - Tomohiro TAKAHASHI
AU - Toshihiro FURUKAWA
PY - 2014
DO - 10.1587/transinf.2014EDL8086
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2014
AB - This paper deals with the problem of reconstructing a high-resolution digital image from a single low-resolution digital image and proposes a new intra-frame super-resolution algorithm based on the mixed lp/l1 norm minimization. Introducing some assumptions, this paper formulates the super-resolution problem as a mixed l0/l1 norm minimization and relaxes the l0 norm term to the lp norm to avoid ill-posedness. A heuristic iterative algorithm is proposed based on the iterative reweighted least squares (IRLS). Numerical examples show that the proposed algorithm achieves super-resolution efficiently.
ER -
English
