Open AccessSingle image dehazing and denoising combining dark channel prior and variational models
Author(s) -
Wang Zhi,
Hou Guojia,
Pan Zhenkuan,
Wang Guodong
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2017.0318
Subject(s) - noise reduction , computation , computer science , channel (broadcasting) , noise (video) , image restoration , transmission (telecommunications) , algorithm , enhanced data rates for gsm evolution , reduction (mathematics) , artificial intelligence , image (mathematics) , video denoising , variation (astronomy) , computer vision , pattern recognition (psychology) , image processing , mathematics , video processing , telecommunications , geometry , video tracking , multiview video coding , physics , astrophysics
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.