An Anisotropic Gaussian Filtering Model for Image De-Hazing

This paper proposes a de-hazing algorithm on the basis of the anisotropic Gaussian filtering method to overcome some essential limitations of the DCP-based (the dark channel prior, DCP) methods, such as halo artifacts and over-saturation problems. In this method, the approximate range of the global atmospheric light A is obtained by using the spatial LOG edge detection method, and the accurate A is acquired by combing binary algorithm. And an anisotropic Gaussian filtering method is adopted to optimize the transmission, which can smooth the rough transmission map, improve the clarity of image details and inhibit halo artifact and over-saturation effect. With the accurate A and optimized transmission, the de-hazing results can be acquired on the basis of the atmospheric scattering model. The processing effect is verified by our own fog image data set constructed in real life or downloaded from the Internet. The visual effect of our method is more natural in brightness, contrast and detail recovery. Besides, the indicators SSIM, PSNR, Lum , Con , Inf , e and r are relatively high, and indicators MSE and \varepsilon are relatively low. Therefore, subjective and objective experimental results demonstrate that our method outperforms four state-of-the-art methods in terms of visual sense definition, robustness and time efficiency.