IEICE TRANSACTIONS on Fundamentals
A Kalman Filter-Based Method for Restoration of Images Obtained by an In-Vehicle Camera in Foggy Conditions
Summary :
In this paper, a Kalman filter-based method for restoration of video images acquired by an in-vehicle camera in foggy conditions is proposed. In order to realize Kalman filter-based restoration, the proposed method clips local blocks from the target frame by using a sliding window and regards the intensities in each block as elements of the state variable of the Kalman filter. Furthermore, the proposed method designs the following two models for restoration of foggy images. The first one is an observation model, which represents a fog deterioration model. The proposed method automatically determines all parameters of the fog deterioration model from only the foggy images to design the observation model. The second one is a non-linear state transition model, which represents the target frame in the original video image from its previous frame based on motion vectors. By utilizing the observation and state transition models, the correlation between successive frames can be effectively utilized for restoration, and accurate restoration of images obtained in foggy conditions can be achieved. Experimental results show that the proposed method has better performance than that of the traditional method based on the fog deterioration model.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.2 pp.577-584
- Publication Date
- 2009/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E92.A.577
- Type of Manuscript
- PAPER
- Category
- Image
Authors
Keyword
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Tomoki HIRAMATSU, Takahiro OGAWA, Miki HASEYAMA, "A Kalman Filter-Based Method for Restoration of Images Obtained by an In-Vehicle Camera in Foggy Conditions" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 2, pp. 577-584, February 2009, doi: 10.1587/transfun.E92.A.577.
Abstract: In this paper, a Kalman filter-based method for restoration of video images acquired by an in-vehicle camera in foggy conditions is proposed. In order to realize Kalman filter-based restoration, the proposed method clips local blocks from the target frame by using a sliding window and regards the intensities in each block as elements of the state variable of the Kalman filter. Furthermore, the proposed method designs the following two models for restoration of foggy images. The first one is an observation model, which represents a fog deterioration model. The proposed method automatically determines all parameters of the fog deterioration model from only the foggy images to design the observation model. The second one is a non-linear state transition model, which represents the target frame in the original video image from its previous frame based on motion vectors. By utilizing the observation and state transition models, the correlation between successive frames can be effectively utilized for restoration, and accurate restoration of images obtained in foggy conditions can be achieved. Experimental results show that the proposed method has better performance than that of the traditional method based on the fog deterioration model.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.577/_p
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@ARTICLE{e92-a_2_577,
author={Tomoki HIRAMATSU, Takahiro OGAWA, Miki HASEYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Kalman Filter-Based Method for Restoration of Images Obtained by an In-Vehicle Camera in Foggy Conditions},
year={2009},
volume={E92-A},
number={2},
pages={577-584},
abstract={In this paper, a Kalman filter-based method for restoration of video images acquired by an in-vehicle camera in foggy conditions is proposed. In order to realize Kalman filter-based restoration, the proposed method clips local blocks from the target frame by using a sliding window and regards the intensities in each block as elements of the state variable of the Kalman filter. Furthermore, the proposed method designs the following two models for restoration of foggy images. The first one is an observation model, which represents a fog deterioration model. The proposed method automatically determines all parameters of the fog deterioration model from only the foggy images to design the observation model. The second one is a non-linear state transition model, which represents the target frame in the original video image from its previous frame based on motion vectors. By utilizing the observation and state transition models, the correlation between successive frames can be effectively utilized for restoration, and accurate restoration of images obtained in foggy conditions can be achieved. Experimental results show that the proposed method has better performance than that of the traditional method based on the fog deterioration model.},
keywords={},
doi={10.1587/transfun.E92.A.577},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - A Kalman Filter-Based Method for Restoration of Images Obtained by an In-Vehicle Camera in Foggy Conditions
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 577
EP - 584
AU - Tomoki HIRAMATSU
AU - Takahiro OGAWA
AU - Miki HASEYAMA
PY - 2009
DO - 10.1587/transfun.E92.A.577
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2009
AB - In this paper, a Kalman filter-based method for restoration of video images acquired by an in-vehicle camera in foggy conditions is proposed. In order to realize Kalman filter-based restoration, the proposed method clips local blocks from the target frame by using a sliding window and regards the intensities in each block as elements of the state variable of the Kalman filter. Furthermore, the proposed method designs the following two models for restoration of foggy images. The first one is an observation model, which represents a fog deterioration model. The proposed method automatically determines all parameters of the fog deterioration model from only the foggy images to design the observation model. The second one is a non-linear state transition model, which represents the target frame in the original video image from its previous frame based on motion vectors. By utilizing the observation and state transition models, the correlation between successive frames can be effectively utilized for restoration, and accurate restoration of images obtained in foggy conditions can be achieved. Experimental results show that the proposed method has better performance than that of the traditional method based on the fog deterioration model.
ER -
English
