Single image dehazing algorithm using wavelet decomposition and fast kernel regression model

Xie Cong-Hua; Qiao Wei-Wei; Zhang Xiu-Xiang; Zhu Feng

doi:10.1117/1.JEI.25.4.043003

11 July 2016 Single image dehazing algorithm using wavelet decomposition and fast kernel regression model

Xie Cong-Hua, Qiao Wei-Wei, Zhang Xiu-Xiang, Zhu Feng

Author Affiliations +

Funded by: Science and Technology Program of Suzhou in China, Natural Science Foundation of Jiangsu Province, Natural Science Foundation, Natural Science Foundation of Jiangsu Province in China

Journal of Electronic Imaging, Vol. 25, Issue 4, 043003 (July 2016). https://doi.org/10.1117/1.JEI.25.4.043003

Abstract

In order to address the problems of discontinuity and block effect for the dehazing method based on dark channel prior, we improved this method using wavelet decomposition, fast kernel regression model, and bicubic interpolation. First, spatial resolution of the hazy image was reduced by the downsampling method with Haar wavelet decomposition. Second, the fast kernel regression model was proposed to smooth the central transmission with local neighbor transmissions. Last, the smoothed transmission for the approximation image was resized to the hazy image by the bicubic interpolation method. Experiments were carried out on synthetic hazy images with known ground truth and real-world hazy images without ground truth. The regions of sudden change of depth in the dehazed images by our method were more smooth and continuous than those of several state-of-the-art methods, and contrast of our method was higher than that of other methods. Indexes based on the concept of visibility level, mean squared error, and structural similarity of our method were better than those of other methods.

Citation Download Citation

Xie Cong-Hua, Qiao Wei-Wei, Zhang Xiu-Xiang, and Zhu Feng "Single image dehazing algorithm using wavelet decomposition and fast kernel regression model," Journal of Electronic Imaging 25(4), 043003 (11 July 2016). https://doi.org/10.1117/1.JEI.25.4.043003

Published: 11 July 2016

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