Abstract: The existing defogging algorithms of underground coal mine images mainly include defogging algorithm based on image enhancement, defogging algorithm based on CNN and defogging algorithm based on physical model. The former two have poor defogging effect and are prone to over-exposure. The physical model-based defogging algorithm processes the dust and fog according to the atmospheric scattering model. However, the dark channel-based atmospheric light value estimation method is applied to the underground coal mine environment, and the selected atmospheric light value will be small. The problems of image overexposure, incapability of inhibiting point light source irradiation are easily caused. In order to solve the above problems, the image defogging algorithm based on dark primary color prior (He algorithm) and the defogging algorithm based on boundary constraint and context regularization (Meng algorithm) are fused. The defogging algorithm of underground coal mine dust and fog image based on boundary constraint is proposed. The method comprises the following steps. Firstly, Gamma correction is performed on the input image. The color channel opening operation processing is performed on the corrected image to obtain the low-resolution pixel block. The maximum brightness value is selected from the low-resolution pixel block as the underground atmospheric light value of the coal mine. Secondly, the Gamma-corrected image is processed by He algorithm and Meng algorithm respectively. The boundary constraint map obtained by Meng algorithm is filtered to obtain a clearer boundary constraint map. And the rough transmittance difference of Meng algorithm and He algorithm is compared and then fused. Finally, the contextual regularization is performed on fused rough transmittance to obtain the refined transmittance. The obtained atmospheric light value and the refined transmittance are used to obtain the defogged image through the atmospheric scattering model. The simulation results show that the proposed defogging algorithm of underground coal mine dust and fog image based on boundary constraint has no problems such as overexposure. The defogging effect is better, the defogged image is brighter and the color is closer to the original image. The peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM) are used to objectively evaluate the defogging effect of the proposed algorithm. The results show that the proposed algorithm has an average improvement of 61.52%, 36.51% and 24.57% in PSNR, SSIM and FSIM compared with the He algorithm. Compared with the algorithm in literature 9, the proposed algorithm has increased by 15.51%, 19.27% and −0.30% on average. Compared with Meng algorithm, the proposed algorithm has increased by 18.93%, 7.19% and 1.21% on average. Compared with the algorithm in literature 11, the proposed algorithm has increased by 18.29%, 10.54% and 1.19% on average. It shows that the proposed algorithm has better defogging effect, brighter image and more details in the underground environment of coal mine.