IEICE TRANSACTIONS on Fundamentals
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Video Smoke Removal from a Single Image Sequence
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In this study, we present a novel method for removing smoke from videos based on a single image sequence. Smoke is a significant artifact in images or videos because it can reduce the visibility in disaster scenes. Our proposed method for removing smoke involves two main processes: (1) the development of a smoke imaging model and (2) smoke removal using spatio-temporal pixel compensation. First, we model the optical phenomena in natural scenes including smoke, which is called a smoke imaging model. Our smoke imaging model is developed by extending conventional haze imaging models. We then remove the smoke from a video in a frame-by-frame manner based on the smoke imaging model. Next, we refine the appearance of the smoke-free video by spatio-temporal pixel compensation, where we align the smoke-free frames using the corresponding pixels. To obtain the corresponding pixels, we use SIFT and color features with distance constraints. Finally, in order to obtain a clear video, we refine the pixel values based on the spatio-temporal weightings of the corresponding pixels in the smoke-free frames. We used simulated and actual smoke videos in our validation experiments. The experimental results demonstrated that our method can obtain effective smoke removal results from dynamic scenes. We also quantitatively assessed our method based on a temporal coherence measure.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E104-A No.6 pp.876-886
- Publication Date
- 2021/06/01
- Publicized
- 2021/01/07
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2020IMP0013
- Type of Manuscript
- Special Section PAPER (Special Section on Image Media Quality)
- Category
Authors
Shiori YAMAGUCHI
Chiba University
Keita HIRAI
Chiba University
Takahiko HORIUCHI
Chiba University
Keyword
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Shiori YAMAGUCHI, Keita HIRAI, Takahiko HORIUCHI, "Video Smoke Removal from a Single Image Sequence" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 6, pp. 876-886, June 2021, doi: 10.1587/transfun.2020IMP0013.
Abstract: In this study, we present a novel method for removing smoke from videos based on a single image sequence. Smoke is a significant artifact in images or videos because it can reduce the visibility in disaster scenes. Our proposed method for removing smoke involves two main processes: (1) the development of a smoke imaging model and (2) smoke removal using spatio-temporal pixel compensation. First, we model the optical phenomena in natural scenes including smoke, which is called a smoke imaging model. Our smoke imaging model is developed by extending conventional haze imaging models. We then remove the smoke from a video in a frame-by-frame manner based on the smoke imaging model. Next, we refine the appearance of the smoke-free video by spatio-temporal pixel compensation, where we align the smoke-free frames using the corresponding pixels. To obtain the corresponding pixels, we use SIFT and color features with distance constraints. Finally, in order to obtain a clear video, we refine the pixel values based on the spatio-temporal weightings of the corresponding pixels in the smoke-free frames. We used simulated and actual smoke videos in our validation experiments. The experimental results demonstrated that our method can obtain effective smoke removal results from dynamic scenes. We also quantitatively assessed our method based on a temporal coherence measure.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020IMP0013/_p
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@ARTICLE{e104-a_6_876,
author={Shiori YAMAGUCHI, Keita HIRAI, Takahiko HORIUCHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Video Smoke Removal from a Single Image Sequence},
year={2021},
volume={E104-A},
number={6},
pages={876-886},
abstract={In this study, we present a novel method for removing smoke from videos based on a single image sequence. Smoke is a significant artifact in images or videos because it can reduce the visibility in disaster scenes. Our proposed method for removing smoke involves two main processes: (1) the development of a smoke imaging model and (2) smoke removal using spatio-temporal pixel compensation. First, we model the optical phenomena in natural scenes including smoke, which is called a smoke imaging model. Our smoke imaging model is developed by extending conventional haze imaging models. We then remove the smoke from a video in a frame-by-frame manner based on the smoke imaging model. Next, we refine the appearance of the smoke-free video by spatio-temporal pixel compensation, where we align the smoke-free frames using the corresponding pixels. To obtain the corresponding pixels, we use SIFT and color features with distance constraints. Finally, in order to obtain a clear video, we refine the pixel values based on the spatio-temporal weightings of the corresponding pixels in the smoke-free frames. We used simulated and actual smoke videos in our validation experiments. The experimental results demonstrated that our method can obtain effective smoke removal results from dynamic scenes. We also quantitatively assessed our method based on a temporal coherence measure.},
keywords={},
doi={10.1587/transfun.2020IMP0013},
ISSN={1745-1337},
month={June},}
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TY - JOUR
TI - Video Smoke Removal from a Single Image Sequence
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 876
EP - 886
AU - Shiori YAMAGUCHI
AU - Keita HIRAI
AU - Takahiko HORIUCHI
PY - 2021
DO - 10.1587/transfun.2020IMP0013
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2021
AB - In this study, we present a novel method for removing smoke from videos based on a single image sequence. Smoke is a significant artifact in images or videos because it can reduce the visibility in disaster scenes. Our proposed method for removing smoke involves two main processes: (1) the development of a smoke imaging model and (2) smoke removal using spatio-temporal pixel compensation. First, we model the optical phenomena in natural scenes including smoke, which is called a smoke imaging model. Our smoke imaging model is developed by extending conventional haze imaging models. We then remove the smoke from a video in a frame-by-frame manner based on the smoke imaging model. Next, we refine the appearance of the smoke-free video by spatio-temporal pixel compensation, where we align the smoke-free frames using the corresponding pixels. To obtain the corresponding pixels, we use SIFT and color features with distance constraints. Finally, in order to obtain a clear video, we refine the pixel values based on the spatio-temporal weightings of the corresponding pixels in the smoke-free frames. We used simulated and actual smoke videos in our validation experiments. The experimental results demonstrated that our method can obtain effective smoke removal results from dynamic scenes. We also quantitatively assessed our method based on a temporal coherence measure.
ER -
English
