Total generalized variation-regularized variational model for single image dehazing

Qiao-Ling Shu; Chuan-Sheng Wu; Qiu-Xiang Zhong; Ryan Wen Liu

doi:10.1117/12.2302936

10 April 2018 Total generalized variation-regularized variational model for single image dehazing

Qiao-Ling Shu, Chuan-Sheng Wu, Qiu-Xiang Zhong, Ryan Wen Liu

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106152M (2018) https://doi.org/10.1117/12.2302936
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Imaging quality is often significantly degraded under hazy weather condition. The purpose of this paper is to recover the latent sharp image from its hazy version. It is well known that the accurate estimation of depth information could assist in improving dehazing performance. In this paper, a detail-preserving variational model was proposed to simultaneously estimate haze-free image and depth map. In particular, the total variation (TV) and total generalized variation (TGV) regularizers were introduced to restrain haze-free image and depth map, respectively. The resulting nonsmooth optimization problem was efficiently solved using the alternating direction method of multipliers (ADMM). Comprehensive experiments have been conducted on realistic datasets to compare our proposed method with several state-of-the-art dehazing methods. Results have illustrated the superior performance of the proposed method in terms of visual quality evaluation.

Citation Download Citation

Qiao-Ling Shu, Chuan-Sheng Wu, Qiu-Xiang Zhong, and Ryan Wen Liu "Total generalized variation-regularized variational model for single image dehazing", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152M (10 April 2018); https://doi.org/10.1117/12.2302936

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