IEICE TRANSACTIONS on Fundamentals
An Edge-Preserving Super-Precision for Simultaneous Enhancement of Spacial and Grayscale Resolutions
Summary :
In this paper, we propose a method that recovers a smooth high-resolution image from several blurred and roughly quantized low-resolution images. For compensation of the quantization effect we introduce measurements of smoothness, Huber function that is originally used for suppression of block noises in a JPEG compressed image [Schultz & Stevenson '94] and a smoothed version of total variation. With a simple operator that approximates the convex projection onto constraint set defined for each quantized image [Hasegawa et al. '05], we propose a method that minimizes these cost functions, which are smooth convex functions, over the intersection of all constraint sets, i.e. the set of all images satisfying all quantization constraints simultaneously, by using hybrid steepest descent method [Yamada & Ogura '04]. Finally in the numerical example we compare images derived by the proposed method, Projections Onto Convex Sets (POCS) based conventinal method, and generalized proposed method minimizing energy of output of Laplacian.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E91-A No.2 pp.673-681
- Publication Date
- 2008/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e91-a.2.673
- Type of Manuscript
- PAPER
- Category
- Image
Authors
Keyword
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Hiroshi HASEGAWA, Toshinori OHTSUKA, Isao YAMADA, Kohichi SAKANIWA, "An Edge-Preserving Super-Precision for Simultaneous Enhancement of Spacial and Grayscale Resolutions" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 2, pp. 673-681, February 2008, doi: 10.1093/ietfec/e91-a.2.673.
Abstract: In this paper, we propose a method that recovers a smooth high-resolution image from several blurred and roughly quantized low-resolution images. For compensation of the quantization effect we introduce measurements of smoothness, Huber function that is originally used for suppression of block noises in a JPEG compressed image [Schultz & Stevenson '94] and a smoothed version of total variation. With a simple operator that approximates the convex projection onto constraint set defined for each quantized image [Hasegawa et al. '05], we propose a method that minimizes these cost functions, which are smooth convex functions, over the intersection of all constraint sets, i.e. the set of all images satisfying all quantization constraints simultaneously, by using hybrid steepest descent method [Yamada & Ogura '04]. Finally in the numerical example we compare images derived by the proposed method, Projections Onto Convex Sets (POCS) based conventinal method, and generalized proposed method minimizing energy of output of Laplacian.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.2.673/_p
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@ARTICLE{e91-a_2_673,
author={Hiroshi HASEGAWA, Toshinori OHTSUKA, Isao YAMADA, Kohichi SAKANIWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Edge-Preserving Super-Precision for Simultaneous Enhancement of Spacial and Grayscale Resolutions},
year={2008},
volume={E91-A},
number={2},
pages={673-681},
abstract={In this paper, we propose a method that recovers a smooth high-resolution image from several blurred and roughly quantized low-resolution images. For compensation of the quantization effect we introduce measurements of smoothness, Huber function that is originally used for suppression of block noises in a JPEG compressed image [Schultz & Stevenson '94] and a smoothed version of total variation. With a simple operator that approximates the convex projection onto constraint set defined for each quantized image [Hasegawa et al. '05], we propose a method that minimizes these cost functions, which are smooth convex functions, over the intersection of all constraint sets, i.e. the set of all images satisfying all quantization constraints simultaneously, by using hybrid steepest descent method [Yamada & Ogura '04]. Finally in the numerical example we compare images derived by the proposed method, Projections Onto Convex Sets (POCS) based conventinal method, and generalized proposed method minimizing energy of output of Laplacian.},
keywords={},
doi={10.1093/ietfec/e91-a.2.673},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - An Edge-Preserving Super-Precision for Simultaneous Enhancement of Spacial and Grayscale Resolutions
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 673
EP - 681
AU - Hiroshi HASEGAWA
AU - Toshinori OHTSUKA
AU - Isao YAMADA
AU - Kohichi SAKANIWA
PY - 2008
DO - 10.1093/ietfec/e91-a.2.673
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2008
AB - In this paper, we propose a method that recovers a smooth high-resolution image from several blurred and roughly quantized low-resolution images. For compensation of the quantization effect we introduce measurements of smoothness, Huber function that is originally used for suppression of block noises in a JPEG compressed image [Schultz & Stevenson '94] and a smoothed version of total variation. With a simple operator that approximates the convex projection onto constraint set defined for each quantized image [Hasegawa et al. '05], we propose a method that minimizes these cost functions, which are smooth convex functions, over the intersection of all constraint sets, i.e. the set of all images satisfying all quantization constraints simultaneously, by using hybrid steepest descent method [Yamada & Ogura '04]. Finally in the numerical example we compare images derived by the proposed method, Projections Onto Convex Sets (POCS) based conventinal method, and generalized proposed method minimizing energy of output of Laplacian.
ER -
English
