IEICE TRANSACTIONS on Information
Variational-Bayesian Single-Image Devignetting
Summary :
Vignetting is a common type of image degradation that makes peripheral parts of an image darker than the central part. Single-image devignetting aims to remove undesirable vignetting from an image without resorting to calibration, thereby providing high-quality images required for a wide range of applications. Previous studies into single-image devignetting have focused on the estimation of vignetting functions under the assumption that degradation other than vignetting is negligible. However, noise in real-world observations remains unremoved after inversion of vignetting, and prevents stable estimation of vignetting functions, thereby resulting in low quality of restored images. In this paper, we introduce a methodology of image restoration based on variational Bayes (VB) to devignetting, aiming at high-quality devignetting in the presence of noise. Through VB inference, we jointly estimate a vignetting function and a latent image free from both vignetting and noise, using a general image prior for noise removal. Compared with state-of-the-art methods, the proposed VB approach to single-image devignetting maintains effectiveness in the presence of noise, as we demonstrate experimentally.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.9 pp.2368-2380
- Publication Date
- 2018/09/01
- Publicized
- 2018/06/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7393
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
Authors
Motoharu SONOGASHIRA
Kyoto University
Masaaki IIYAMA
Kyoto University
Michihiko MINOH
Kyoto University
Keyword
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Motoharu SONOGASHIRA, Masaaki IIYAMA, Michihiko MINOH, "Variational-Bayesian Single-Image Devignetting" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 9, pp. 2368-2380, September 2018, doi: 10.1587/transinf.2017EDP7393.
Abstract: Vignetting is a common type of image degradation that makes peripheral parts of an image darker than the central part. Single-image devignetting aims to remove undesirable vignetting from an image without resorting to calibration, thereby providing high-quality images required for a wide range of applications. Previous studies into single-image devignetting have focused on the estimation of vignetting functions under the assumption that degradation other than vignetting is negligible. However, noise in real-world observations remains unremoved after inversion of vignetting, and prevents stable estimation of vignetting functions, thereby resulting in low quality of restored images. In this paper, we introduce a methodology of image restoration based on variational Bayes (VB) to devignetting, aiming at high-quality devignetting in the presence of noise. Through VB inference, we jointly estimate a vignetting function and a latent image free from both vignetting and noise, using a general image prior for noise removal. Compared with state-of-the-art methods, the proposed VB approach to single-image devignetting maintains effectiveness in the presence of noise, as we demonstrate experimentally.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7393/_p
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@ARTICLE{e101-d_9_2368,
author={Motoharu SONOGASHIRA, Masaaki IIYAMA, Michihiko MINOH, },
journal={IEICE TRANSACTIONS on Information},
title={Variational-Bayesian Single-Image Devignetting},
year={2018},
volume={E101-D},
number={9},
pages={2368-2380},
abstract={Vignetting is a common type of image degradation that makes peripheral parts of an image darker than the central part. Single-image devignetting aims to remove undesirable vignetting from an image without resorting to calibration, thereby providing high-quality images required for a wide range of applications. Previous studies into single-image devignetting have focused on the estimation of vignetting functions under the assumption that degradation other than vignetting is negligible. However, noise in real-world observations remains unremoved after inversion of vignetting, and prevents stable estimation of vignetting functions, thereby resulting in low quality of restored images. In this paper, we introduce a methodology of image restoration based on variational Bayes (VB) to devignetting, aiming at high-quality devignetting in the presence of noise. Through VB inference, we jointly estimate a vignetting function and a latent image free from both vignetting and noise, using a general image prior for noise removal. Compared with state-of-the-art methods, the proposed VB approach to single-image devignetting maintains effectiveness in the presence of noise, as we demonstrate experimentally.},
keywords={},
doi={10.1587/transinf.2017EDP7393},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Variational-Bayesian Single-Image Devignetting
T2 - IEICE TRANSACTIONS on Information
SP - 2368
EP - 2380
AU - Motoharu SONOGASHIRA
AU - Masaaki IIYAMA
AU - Michihiko MINOH
PY - 2018
DO - 10.1587/transinf.2017EDP7393
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2018
AB - Vignetting is a common type of image degradation that makes peripheral parts of an image darker than the central part. Single-image devignetting aims to remove undesirable vignetting from an image without resorting to calibration, thereby providing high-quality images required for a wide range of applications. Previous studies into single-image devignetting have focused on the estimation of vignetting functions under the assumption that degradation other than vignetting is negligible. However, noise in real-world observations remains unremoved after inversion of vignetting, and prevents stable estimation of vignetting functions, thereby resulting in low quality of restored images. In this paper, we introduce a methodology of image restoration based on variational Bayes (VB) to devignetting, aiming at high-quality devignetting in the presence of noise. Through VB inference, we jointly estimate a vignetting function and a latent image free from both vignetting and noise, using a general image prior for noise removal. Compared with state-of-the-art methods, the proposed VB approach to single-image devignetting maintains effectiveness in the presence of noise, as we demonstrate experimentally.
ER -
English
