Weight Optimization for Multiple Image Integration and Its Applications

Ryo MATSUOKA; Tomohiro YAMAUCHI; Tatsuya BABA; Masahiro OKUDA

doi:10.1587/transinf.2015EDP7192

Weight Optimization for Multiple Image Integration and Its Applications

Ryo MATSUOKA, Tomohiro YAMAUCHI, Tatsuya BABA, Masahiro OKUDA

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Summary :

We propose an image restoration technique that uses multiple image integration. The detail of the dark area when acquiring a dark scene is often deteriorated by sensor noise. Simple image integration inherently has the capability of reducing random noises, but it is especially insufficient in scenes that have a dark area. We introduce a novel image integration technique that optimizes the weights for the integration. We find the optimal weight map by solving a convex optimization problem for the weight optimization. Additionally, we apply the proposed weight optimization scheme to a single-image super-resolution problem, where we slightly modify the weight optimization problem to estimate the high-resolution image from a single low-resolution one. We use some of our experimental results to show that the weight optimization significantly improves the denoising and super-resolution performances.

Publication: IEICE TRANSACTIONS on Information Vol.E99-D No.1 pp.228-235

Publication Date: 2016/01/01

Publicized: 2015/10/06

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2015EDP7192

Type of Manuscript: PAPER

Category: Image Processing and Video Processing

Authors

Ryo MATSUOKA
  the University of Kitakyushu
Tomohiro YAMAUCHI
  the University of Kitakyushu
Tatsuya BABA
  the University of Kitakyushu
Masahiro OKUDA
  the University of Kitakyushu

Keyword

denoising, convex optimization, high dynamic range images, super-resolution

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Ryo MATSUOKA, Tomohiro YAMAUCHI, Tatsuya BABA, Masahiro OKUDA, "Weight Optimization for Multiple Image Integration and Its Applications" in IEICE TRANSACTIONS on Information, vol. E99-D, no. 1, pp. 228-235, January 2016, doi: 10.1587/transinf.2015EDP7192.
Abstract: We propose an image restoration technique that uses multiple image integration. The detail of the dark area when acquiring a dark scene is often deteriorated by sensor noise. Simple image integration inherently has the capability of reducing random noises, but it is especially insufficient in scenes that have a dark area. We introduce a novel image integration technique that optimizes the weights for the integration. We find the optimal weight map by solving a convex optimization problem for the weight optimization. Additionally, we apply the proposed weight optimization scheme to a single-image super-resolution problem, where we slightly modify the weight optimization problem to estimate the high-resolution image from a single low-resolution one. We use some of our experimental results to show that the weight optimization significantly improves the denoising and super-resolution performances.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDP7192/_p

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@ARTICLE{e99-d_1_228,
author={Ryo MATSUOKA, Tomohiro YAMAUCHI, Tatsuya BABA, Masahiro OKUDA, },
journal={IEICE TRANSACTIONS on Information},
title={Weight Optimization for Multiple Image Integration and Its Applications},
year={2016},
volume={E99-D},
number={1},
pages={228-235},
abstract={We propose an image restoration technique that uses multiple image integration. The detail of the dark area when acquiring a dark scene is often deteriorated by sensor noise. Simple image integration inherently has the capability of reducing random noises, but it is especially insufficient in scenes that have a dark area. We introduce a novel image integration technique that optimizes the weights for the integration. We find the optimal weight map by solving a convex optimization problem for the weight optimization. Additionally, we apply the proposed weight optimization scheme to a single-image super-resolution problem, where we slightly modify the weight optimization problem to estimate the high-resolution image from a single low-resolution one. We use some of our experimental results to show that the weight optimization significantly improves the denoising and super-resolution performances.},
keywords={},
doi={10.1587/transinf.2015EDP7192},
ISSN={1745-1361},
month={January},}

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TY - JOUR
TI - Weight Optimization for Multiple Image Integration and Its Applications
T2 - IEICE TRANSACTIONS on Information
SP - 228
EP - 235
AU - Ryo MATSUOKA
AU - Tomohiro YAMAUCHI
AU - Tatsuya BABA
AU - Masahiro OKUDA
PY - 2016
DO - 10.1587/transinf.2015EDP7192
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2016
AB - We propose an image restoration technique that uses multiple image integration. The detail of the dark area when acquiring a dark scene is often deteriorated by sensor noise. Simple image integration inherently has the capability of reducing random noises, but it is especially insufficient in scenes that have a dark area. We introduce a novel image integration technique that optimizes the weights for the integration. We find the optimal weight map by solving a convex optimization problem for the weight optimization. Additionally, we apply the proposed weight optimization scheme to a single-image super-resolution problem, where we slightly modify the weight optimization problem to estimate the high-resolution image from a single low-resolution one. We use some of our experimental results to show that the weight optimization significantly improves the denoising and super-resolution performances.
ER -