A traffic image dehaze method based on adaptive transmittance estimation with multi-scale window

Aiming at the halo effect and the color distortion of bright areas when using traditional dark priori image defogging algorithms, we propose a traffic image dehaze method based on adaptive transmittance estimation with multi-scale window in this paper. Firstly, a new 8-direction edge detection operator is used to detect abrupt changes in field depth in images. According to the dark channel prior theory and the abrupt change of field depth obtained in the previous step, a 5×5 window is used in the larger area of field depth transformation and a 15×15 window is used in the smaller area to obtain a dark primary color estimation image. At the same time, targetting the problem of inaccurate estimation of transmittance when there is a white area in the close-range region due to the dark channel priori principle, we introduce an adaptive transmittance restoration method. An edge-enhanced dark image is obtained by using a guide filter, and the texture difference between the edge-enhanced dark image and the original dark primary image is used to correct the transmittance in the close-range region, and then to complete image dehazing. The experimental results show that the halo phenomenon exists in both the bilateral filter and the gradient bilateral filter, and the color distortion is serious in the bright area containing white objects, causing the objective evaluation index to be meaningless. Compared with the guide filter, the indexes of the dehazing algorithm used in this paper show improvement, wherein the average gradient increased by 8.305%, the PSNR increased by 12.455% and the edge strength factor increased by 7.77%. The algorithm can effectively solve issues arising from the halo effect and color distortion in bright areas in restored images while providing a more effective dehazing effect.