research-article

Haze Removal Algorithm Using Color Attenuation Prior and Guided Filter

Authors:

Shangbo ZhouAuthors Info & Claims

ICMSSP '18: Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing

Pages 41 - 45

https://doi.org/10.1145/3220162.3220165

Published: 28 April 2018 Publication History

Abstract

In this paper, we describe the formatting guidelines for ACM SIG Proceedings. In this paper, we mainly introduce the haze removal algorithm which using color attenuation prior. We also propose a novel solution in the case of particular situations. Moreover, in order to make the algorithm more accurate, we propose a hierarchical searching method based on the quad-tree subdivision for estimating atmospheric light. In addition, we employ the shiftable window scheme to improve the guided filter, so that it can refine the depth map while effectively protecting image edge. Experimental result shows that our proposed method not only can obviously remove the haze from the haze image, but also the performance is more reliable in the case of particular situations.

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Cited By

Pal THalder MBarua S(2023)A deep learning model to detect foggy images for vision enhancementThe Imaging Science Journal10.1080/13682199.2023.218542971:6(484-498)Online publication date: 9-Mar-2023
https://doi.org/10.1080/13682199.2023.2185429
Pal THalder MBarua S(2023)A transmission model based deep neural network for image dehazingMultimedia Tools and Applications10.1007/s11042-023-17010-483:13(39255-39281)Online publication date: 7-Oct-2023
https://doi.org/10.1007/s11042-023-17010-4
Yang YZhu YYang QJiang Z(2021)Research on Hardware Implementation Technology of Dark Channel Priori Dehazing Algorithm Based on PYNQ2021 3rd International Conference on Applied Machine Learning (ICAML)10.1109/ICAML54311.2021.00016(37-42)Online publication date: Jul-2021
https://doi.org/10.1109/ICAML54311.2021.00016

Index Terms

Haze Removal Algorithm Using Color Attenuation Prior and Guided Filter
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

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cover image ACM Other conferences

ICMSSP '18: Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing

April 2018

168 pages

ISBN:9781450364577

DOI:10.1145/3220162

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 April 2018

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ICMSSP '18

ICMSSP '18: 2018 3rd International Conference on Multimedia Systems and Signal Processing

April 28 - 30, 2018

Shenzhen, China

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Cited By

Pal THalder MBarua S(2023)A deep learning model to detect foggy images for vision enhancementThe Imaging Science Journal10.1080/13682199.2023.218542971:6(484-498)Online publication date: 9-Mar-2023
https://doi.org/10.1080/13682199.2023.2185429
Pal THalder MBarua S(2023)A transmission model based deep neural network for image dehazingMultimedia Tools and Applications10.1007/s11042-023-17010-483:13(39255-39281)Online publication date: 7-Oct-2023
https://doi.org/10.1007/s11042-023-17010-4
Yang YZhu YYang QJiang Z(2021)Research on Hardware Implementation Technology of Dark Channel Priori Dehazing Algorithm Based on PYNQ2021 3rd International Conference on Applied Machine Learning (ICAML)10.1109/ICAML54311.2021.00016(37-42)Online publication date: Jul-2021
https://doi.org/10.1109/ICAML54311.2021.00016

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