Abstract: Addressing issues such as sky region distortion, color bias in results, and incomplete defogging in the process of image dehazing, an adaptive Gaussian function dehazing algorithm based on channel difference prior is proposed. Starting from the essence of degradation in foggy images, a statistical prior reflecting the intrinsic relationship between foggy and haze-free images, namely the channel difference prior, is introduced. Using this prior, a set of equations for foggy and haze-free images is established. The depth of field is approximately estimated using the difference between saturation and brightness of the foggy image. An adaptive standard deviation Gaussian function is designed to solve the equations and obtain the initial transmission map. After normalization, the "fog addition" phenomenon in bright regions is addressed, and joint bilateral filtering is used to further optimize the transmission map. Multi-scale filtering and geometric mean optimization are applied to refine the local atmospheric light, and the dehazed image is obtained by combining the atmospheric scattering model. Experimental results show that the proposed algorithm avoids distortion in the sky region, preserves rich detail information, and achieves significant dehazing effects while maintaining good image color.

Key words: image dehazing, channel difference prior, adaptive Gaussian function, local atmospheric light