RT Journal Article
A1 Zhi Wang
A1 Guojia Hou
A1 Zhenkuan Pan
A1 Guodong Wang

PB iet
T1 Single image dehazing and denoising combining dark channel prior and variational models
JN IET Computer Vision
VO 12
IS 4
SP 393
OP 402
AB 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.
K1 single colour image dehazing model
K1 dark channel prior
K1 MTV regulariser
K1 transmission map estimation
K1 edge-preserving property
K1 DCP
K1 haze removal
K1 CTV regulariser
K1 total variation model
K1 layered total variation regulariser
K1 Bregman iterative parameter
K1 computation efficiency
K1 LTV regulariser
K1 fast split Bregman algorithm
K1 image denoising model
K1 TV model
K1 noise removal
K1 colour total variation regulariser
K1 multichannel total variation regulariser
DO https://doi.org/10.1049/iet-cvi.2017.0318
UL https://digital-library.theiet.org/;jsessionid=19dg5b8te87v6.x-iet-live-01content/journals/10.1049/iet-cvi.2017.0318
LA English
SN 1751-9632
YR 2018
OL EN