A Point Light Source Interference Removal Method for Image Dehazing

Yan, Y; Zhang, S; Ju, M; Ren, W; Wang, R; Guo, Y

A Point Light Source Interference Removal Method for Image Dehazing

Yan, Y Zhang, S Ju, M

Ren, W Wang, R Guo, Y

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, 2020-June, pp. 3817-3825
Issue Date:: 2020-07-28

Closed Access

	Filename	Description	Size
	09151083.pdf	Published Version	2.65 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Yan, Y
dc.contributor.author	Zhang, S
dc.contributor.author	Ju, M https://orcid.org/0000-0003-4378-3781
dc.contributor.author	Ren, W
dc.contributor.author	Wang, R
dc.contributor.author	Guo, Y
dc.date	2020-06-14
dc.date.accessioned	2021-04-15T10:23:03Z
dc.date.available	2021-04-15T10:23:03Z
dc.date.issued	2020-07-28
dc.identifier.citation	2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, 2020-June, pp. 3817-3825
dc.identifier.isbn	978-1-7281-9361-8
dc.identifier.issn	2160-7508
dc.identifier.issn	2160-7516
dc.identifier.uri	http://hdl.handle.net/10453/148143
dc.description.abstract	Single image haze removal has been a challenging problem and the performance of the most existing dehazing methods is degraded when point light sources exist in the hazy image. In this paper, we propose a point light source interference removal method (PLiSIR) to reduce the interferences when estimating the atmospheric light. According to our observation, the pixel intensity around the point light sources can be modeled approximately by Gaussian distribution. The locations of the interfered pixels are obtained reasonably regardless of the specific number of light sources. A binary masking map is then created for distinguishing whether the pixel is affected by light sources and thus PLiSIR can be adopted to dehazing algorithms by removing the interfered pixels, during the estimation of the atmospheric light. To demonstrate how to apply PLiSIR to different algorithms, we select the dark channel prior dehazing method (DCP) and the color attenuation prior dehazing method (CAP) as two carrier methods and introduce the adaptations accordingly. Experimental results indicate that the PLiSIR can assist DCP and CAP to better estimate the atmospheric light, and thus generate better dehazing results compared to the original DCP and CAP methods. Moreover, PLiSIR also helps DCP and CAP to simplify the parameter adjustment process of the guided filter. At last, we compare our modified DCP approach (which we refer to PLiSIR-DCP) with the state-of-the-art nighttime dehazing algorithm to present an approach which is suitable for both daytime and nighttime haze removal.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
dc.relation.ispartof	2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
dc.relation.isbasedon	10.1109/cvprw50498.2020.00445
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Point Light Source Interference Removal Method for Image Dehazing
dc.type	Conference Proceeding
utslib.citation.volume	2020-June
utslib.location.activity	Seattle, WA, USA
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2021-04-15T10:23:01Z
pubs.finish-date	2020-06-19
pubs.publication-status	Published
pubs.start-date	2020-06-14
pubs.volume	2020-June

Abstract:

Single image haze removal has been a challenging problem and the performance of the most existing dehazing methods is degraded when point light sources exist in the hazy image. In this paper, we propose a point light source interference removal method (PLiSIR) to reduce the interferences when estimating the atmospheric light. According to our observation, the pixel intensity around the point light sources can be modeled approximately by Gaussian distribution. The locations of the interfered pixels are obtained reasonably regardless of the specific number of light sources. A binary masking map is then created for distinguishing whether the pixel is affected by light sources and thus PLiSIR can be adopted to dehazing algorithms by removing the interfered pixels, during the estimation of the atmospheric light. To demonstrate how to apply PLiSIR to different algorithms, we select the dark channel prior dehazing method (DCP) and the color attenuation prior dehazing method (CAP) as two carrier methods and introduce the adaptations accordingly. Experimental results indicate that the PLiSIR can assist DCP and CAP to better estimate the atmospheric light, and thus generate better dehazing results compared to the original DCP and CAP methods. Moreover, PLiSIR also helps DCP and CAP to simplify the parameter adjustment process of the guided filter. At last, we compare our modified DCP approach (which we refer to PLiSIR-DCP) with the state-of-the-art nighttime dehazing algorithm to present an approach which is suitable for both daytime and nighttime haze removal.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148143