NDLI: Saliency-based dark channel prior model for single image haze removal

Content Provider	IET Digital Library
Author	Zhang, Libao Wang, Shiyi Wang, Xiaohan
Abstract	Images degraded by haze usually have low contrast and fide colours, and thus have bad effects on applications such as object tracking, face recognition, and intelligent surveillance. So the purpose of dehazing is to recover the image contrast without colour distortion. The dark channel prior (DCP) is widely used in the field of haze removal because of its simplicity and effectiveness. However, when faced with bright white objects, DCP overestimates the haze from its true value and thus causes colour distortion. In this study, the authors propose a dehazing model combining saliency detection with DCP to obtain recovered images with little colour distortion. There are three main contributions. First, they introduce a novel saliency detection method, focusing on superpixel intensity contrast, to extract bright white objects in the hazy image. Those objects are not used to estimate the atmospheric light and transmission in the dark channel image. Second, a self-adaptive upper bound is set for the scene radiance to prevent some regions being too bright. Third, they propose a quantitative indicator, colour variance distance, to evaluate the colour restoration. Experimental results show that their proposed model generates less colour distortion and has better comprehensive performance than competing models.
Starting Page	1049
Ending Page	1055
Page Count	7
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 6, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0959
Journal	IET Image Processing
Publisher Date	2018-02-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Atmospheric Light Bright White Object Extraction Colour Distortion Colour Restoration Evaluation Colour Variance Distance Computer Vision And Image Processing Technique Dark Channel Image DCP Dehazing Model Distortion Image Colour Analysis Image Contrast Recovery Image Restoration Optical, Image And Video Signal Processing Quantitative Indicator Saliency Detection Method Saliency-based Dark Channel Prior Model Self-adaptive Upper Bound Single Image Haze Removal Superpixel Intensity Contrast
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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