IEICE TRANSACTIONS on Information
Fusion-Based Edge and Color Recovery Using Weighted Near-Infrared Image and Color Transmission Maps for Robust Haze Removal
Summary :
Various haze removal methods based on the atmospheric scattering model have been presented in recent years. Most methods have targeted strong haze images where light is scattered equally in all color channels. This paper presents a haze removal method using near-infrared (NIR) images for relatively weak haze images. In order to recover the lost edges, the presented method first extracts edges from an appropriately weighted NIR image and fuses it with the color image. By introducing a wavelength-dependent scattering model, our method then estimates the transmission map for each color channel and recovers the color more naturally from the edge-recovered image. Finally, the edge-recovered and the color-recovered images are blended. In this blending process, the regions with high lightness, such as sky and clouds, where unnatural color shifts are likely to occur, are effectively estimated, and the optimal weighting map is obtained. Our qualitative and quantitative evaluations using 59 pairs of color and NIR images demonstrated that our method can recover edges and colors more naturally in weak haze images than conventional methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.10 pp.1661-1672
- Publication Date
- 2023/10/01
- Publicized
- 2023/05/23
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2023PCP0007
- Type of Manuscript
- Special Section PAPER (Special Section on Picture Coding and Image Media Processing)
- Category
Authors
Onhi KATO
Chuo University
Akira KUBOTA
Chuo University
Keyword
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Onhi KATO, Akira KUBOTA, "Fusion-Based Edge and Color Recovery Using Weighted Near-Infrared Image and Color Transmission Maps for Robust Haze Removal" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 10, pp. 1661-1672, October 2023, doi: 10.1587/transinf.2023PCP0007.
Abstract: Various haze removal methods based on the atmospheric scattering model have been presented in recent years. Most methods have targeted strong haze images where light is scattered equally in all color channels. This paper presents a haze removal method using near-infrared (NIR) images for relatively weak haze images. In order to recover the lost edges, the presented method first extracts edges from an appropriately weighted NIR image and fuses it with the color image. By introducing a wavelength-dependent scattering model, our method then estimates the transmission map for each color channel and recovers the color more naturally from the edge-recovered image. Finally, the edge-recovered and the color-recovered images are blended. In this blending process, the regions with high lightness, such as sky and clouds, where unnatural color shifts are likely to occur, are effectively estimated, and the optimal weighting map is obtained. Our qualitative and quantitative evaluations using 59 pairs of color and NIR images demonstrated that our method can recover edges and colors more naturally in weak haze images than conventional methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023PCP0007/_p
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@ARTICLE{e106-d_10_1661,
author={Onhi KATO, Akira KUBOTA, },
journal={IEICE TRANSACTIONS on Information},
title={Fusion-Based Edge and Color Recovery Using Weighted Near-Infrared Image and Color Transmission Maps for Robust Haze Removal},
year={2023},
volume={E106-D},
number={10},
pages={1661-1672},
abstract={Various haze removal methods based on the atmospheric scattering model have been presented in recent years. Most methods have targeted strong haze images where light is scattered equally in all color channels. This paper presents a haze removal method using near-infrared (NIR) images for relatively weak haze images. In order to recover the lost edges, the presented method first extracts edges from an appropriately weighted NIR image and fuses it with the color image. By introducing a wavelength-dependent scattering model, our method then estimates the transmission map for each color channel and recovers the color more naturally from the edge-recovered image. Finally, the edge-recovered and the color-recovered images are blended. In this blending process, the regions with high lightness, such as sky and clouds, where unnatural color shifts are likely to occur, are effectively estimated, and the optimal weighting map is obtained. Our qualitative and quantitative evaluations using 59 pairs of color and NIR images demonstrated that our method can recover edges and colors more naturally in weak haze images than conventional methods.},
keywords={},
doi={10.1587/transinf.2023PCP0007},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Fusion-Based Edge and Color Recovery Using Weighted Near-Infrared Image and Color Transmission Maps for Robust Haze Removal
T2 - IEICE TRANSACTIONS on Information
SP - 1661
EP - 1672
AU - Onhi KATO
AU - Akira KUBOTA
PY - 2023
DO - 10.1587/transinf.2023PCP0007
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2023
AB - Various haze removal methods based on the atmospheric scattering model have been presented in recent years. Most methods have targeted strong haze images where light is scattered equally in all color channels. This paper presents a haze removal method using near-infrared (NIR) images for relatively weak haze images. In order to recover the lost edges, the presented method first extracts edges from an appropriately weighted NIR image and fuses it with the color image. By introducing a wavelength-dependent scattering model, our method then estimates the transmission map for each color channel and recovers the color more naturally from the edge-recovered image. Finally, the edge-recovered and the color-recovered images are blended. In this blending process, the regions with high lightness, such as sky and clouds, where unnatural color shifts are likely to occur, are effectively estimated, and the optimal weighting map is obtained. Our qualitative and quantitative evaluations using 59 pairs of color and NIR images demonstrated that our method can recover edges and colors more naturally in weak haze images than conventional methods.
ER -
English
