Haze removal with fusion of local and non-local statistics

Jie Chen; Cheen Hau Tan; Lap Pui Chau

doi:10.1109/ISCAS45731.2020.9180749

Haze removal with fusion of local and non-local statistics

Jie Chen, Cheen Hau Tan, Lap Pui Chau^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

Most of the outdoor images suffer from contrast degradation caused by fog and haze. Two statistical frameworks have been proposed in recent years that exploit local (dark channel prior) and non-local (haze-lines) characteristics of hazy images for the estimation of scene configurations and the restoration of scene albedo. Both frameworks show intrinsic limitations due to the basic assumptions they rely on. In this paper we propose a novel dehazing method that combines the advantages of local and non-local dehazing methods. Exploiting their complementary statistical properties, we use the local features to regulate the estimation of non-local haze-lines for a better final restoration at challenging regions. Both quantitative and qualitative results validate the effectiveness of our proposed method over state-of-the-art frameworks.

Original language	English
Title of host publication	2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9781728133201
DOIs	https://doi.org/10.1109/ISCAS45731.2020.9180749
Publication status	Published - Oct 2020
Event	52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Virtual, Online Duration: 10 Oct 2020 → 21 Oct 2020 https://iscas2020.org/ https://ieeexplore.ieee.org/xpl/conhome/9179985/proceeding

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2020-October
ISSN (Print)	0271-4310
ISSN (Electronic)	2158-1525

Conference

Conference	52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020
City	Virtual, Online
Period	10/10/20 → 21/10/20
Internet address	https://iscas2020.org/ https://ieeexplore.ieee.org/xpl/conhome/9179985/proceeding

User-Defined Keywords

Dark channel prior
Haze line
Non-local

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10.1109/ISCAS45731.2020.9180749

Cite this

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title = "Haze removal with fusion of local and non-local statistics",

abstract = "Most of the outdoor images suffer from contrast degradation caused by fog and haze. Two statistical frameworks have been proposed in recent years that exploit local (dark channel prior) and non-local (haze-lines) characteristics of hazy images for the estimation of scene configurations and the restoration of scene albedo. Both frameworks show intrinsic limitations due to the basic assumptions they rely on. In this paper we propose a novel dehazing method that combines the advantages of local and non-local dehazing methods. Exploiting their complementary statistical properties, we use the local features to regulate the estimation of non-local haze-lines for a better final restoration at challenging regions. Both quantitative and qualitative results validate the effectiveness of our proposed method over state-of-the-art frameworks.",

keywords = "Dark channel prior, Haze line, Non-local",

author = "Jie Chen and Tan, {Cheen Hau} and Chau, {Lap Pui}",

note = "Funding Information: Jie Chen was with the School of Electrical & Electronic Engineering, Nanyang Technological University. He is now with the Department of Computer Science, Hong Kong Baptist University. The research was partially supported by the ST Engineering-NTU Corporate Lab through the NRF corporate lab ?university scheme. ; 52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020 ; Conference date: 10-10-2020 Through 21-10-2020",

year = "2020",

month = oct,

doi = "10.1109/ISCAS45731.2020.9180749",

language = "English",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

publisher = "IEEE",

booktitle = "2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings",

address = "United States",

url = "https://iscas2020.org/, https://ieeexplore.ieee.org/xpl/conhome/9179985/proceeding",

}

Chen, J, Tan, CH & Chau, LP 2020, Haze removal with fusion of local and non-local statistics. in 2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings., 9180749, Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2020-October, IEEE, 52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020, Virtual, Online, 10/10/20. https://doi.org/10.1109/ISCAS45731.2020.9180749

Haze removal with fusion of local and non-local statistics. / Chen, Jie; Tan, Cheen Hau; Chau, Lap Pui.
2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings. IEEE, 2020. 9180749 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2020-October).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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T1 - Haze removal with fusion of local and non-local statistics

AU - Chen, Jie

AU - Tan, Cheen Hau

AU - Chau, Lap Pui

N1 - Funding Information: Jie Chen was with the School of Electrical & Electronic Engineering, Nanyang Technological University. He is now with the Department of Computer Science, Hong Kong Baptist University. The research was partially supported by the ST Engineering-NTU Corporate Lab through the NRF corporate lab ?university scheme.

PY - 2020/10

Y1 - 2020/10

N2 - Most of the outdoor images suffer from contrast degradation caused by fog and haze. Two statistical frameworks have been proposed in recent years that exploit local (dark channel prior) and non-local (haze-lines) characteristics of hazy images for the estimation of scene configurations and the restoration of scene albedo. Both frameworks show intrinsic limitations due to the basic assumptions they rely on. In this paper we propose a novel dehazing method that combines the advantages of local and non-local dehazing methods. Exploiting their complementary statistical properties, we use the local features to regulate the estimation of non-local haze-lines for a better final restoration at challenging regions. Both quantitative and qualitative results validate the effectiveness of our proposed method over state-of-the-art frameworks.

AB - Most of the outdoor images suffer from contrast degradation caused by fog and haze. Two statistical frameworks have been proposed in recent years that exploit local (dark channel prior) and non-local (haze-lines) characteristics of hazy images for the estimation of scene configurations and the restoration of scene albedo. Both frameworks show intrinsic limitations due to the basic assumptions they rely on. In this paper we propose a novel dehazing method that combines the advantages of local and non-local dehazing methods. Exploiting their complementary statistical properties, we use the local features to regulate the estimation of non-local haze-lines for a better final restoration at challenging regions. Both quantitative and qualitative results validate the effectiveness of our proposed method over state-of-the-art frameworks.

KW - Dark channel prior

KW - Haze line

KW - Non-local

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ER -

Haze removal with fusion of local and non-local statistics

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