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access icon free Single image dehazing and denoising combining dark channel prior and variational models

© The Institution of Engineering and Technology
Received 13/06/2017, Accepted 30/11/2017, Revised 12/11/2017, Published 11/12/2017

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.

Inspec keywords: estimation theory; variational techniques; image denoising; image colour analysis; iterative methods

Other keywords: Bregman iterative parameter; edge-preserving property; dark channel prior; MTV regulariser; single colour image dehazing model; multichannel total variation regulariser; colour total variation regulariser; haze removal; TV model; total variation model; DCP; computation efficiency; layered total variation regulariser; transmission map estimation; LTV regulariser; image denoising model; noise removal; CTV regulariser; fast split Bregman algorithm

Subjects: Other topics in statistics; Interpolation and function approximation (numerical analysis); Other topics in statistics; Optimisation techniques; Optimisation techniques; Interpolation and function approximation (numerical analysis); Optical, image and video signal processing; Computer vision and image processing techniques

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