NDLI: Single image dehazing and denoising combining dark channel prior and variational models

Content Provider	IET Digital Library
Author	Wang, Zhi Hou, Guojia Pan, Zhenkuan Wang, Guodong
Abstract	Single image dehazing and denoising models can simultaneously remove haze and noise with high efficiency. Here, the authors propose three variational models combining the celebrated dark channel prior (DCP) and total variations (TV) models for image dehazing and denoising. The authors firstly estimate the transmission map associated with depth using DCP, then design three variational models for colour image dehazing and denoising based on this estimation and the layered total variation (LTV) regulariser, multichannel total variation (MTV) regulariser, and colour total variation (CTV) regulariser, respectively. In order to improve the computation efficiency of the three models, the authors design their fast split Bregman algorithms via introducing some auxiliary variables and the Bregman iterative parameters. Numerous experiments are presented to compare their denoising effects, edge-preserving properties, and computation efficiencies. To demonstrate the merits of the proposed models, the authors also conduct some comparisons with several existing state-of-the-art methods. Numerical results further prove that the LTV-based model is fastest, and the CTV model is the best for denoising with edge-preserving, and it also leads to the best visually haze-free and noise-free images.
Starting Page	393
Ending Page	402
Page Count	10
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 4, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0318
Journal	IET Computer Vision
Publisher Date	2017-12-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bregman Iterative Parameter Colour Total Variation Regulariser Computation Efficiency Computer Vision And Image Processing Technique CTV Regulariser Dark Channel Prior DCP Edge-preserving Property Estimation Theory Fast Split Bregman Algorithm Haze Removal Image Colour Analysis Image Denoising Image Denoising Model Interpolation And Function Approximation Iterative Method Layered Total Variation Regulariser LTV Regulariser MTV Regulariser Multichannel Total Variation Regulariser Noise Removal Numerical Analysis Optical, Image And Video Signal Processing Optimisation Technique Single Colour Image Dehazing Model Statistics Total Variation Model Transmission Map Estimation TV Model Variational Technique
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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