Abstract: Aiming at the problems of halo effect and color cast of sky area in dark channel prior algorithm, an image dehazing algorithm based on transform domain and adaptive gamma correction is proposed. By transforming atmospheric scattering model to logarithmic domain, combined with the dark channel prior theory, a positive correlation in logarithmic domain is proposed. Then the Gaussian function is used to fit positive correlation to obtain the coarse transmission. At the same time, the hazy image is converted to HSV color space, the brightness component is extracted to construct an adaptive gamma correction factor, the coarse transmission is corrected, and cross bilateral filtering operation is used to further optimize the transmission. Finally, the restoration of the haze-free image is realized by the atmospheric scattering model and the improved local atmospheric light. Experimental results show that the recovered image has rich details and thorough degree of dehazing in comparison to some classic algorithms. Moreover, it is closer to the real scene because of the better color fidelity.

Key words: image dehazing, dark channel prior, logarithmic domain, positive correlation, adaptive correction