Abstract: Objective In coal mines, the uneven distribution of dust haze and complex illumination conditions caused by underground coal mining and dust removal lead to blurred video images, as well as the loss of information and details. Hence, this study proposed a dehazing and enhancement algorithm for heterogeneous images of underground mining environments. Methods Initially, hazy images were segmented into zones with different brightness values, for which the average ambient light intensity of global dark channels was calculated. The calculation results were integrated through weighting with the ambient light of local bright channels, which was obtained using adaptive gamma correction and multiscale Gaussian filtering. Consequently, accurate ambient light intensity estimates were determined. To preserve image details while achieving natural dehazing effects, transmission maps were processed using multiscale fusion correction technology and were then refined using joint bilateral filtering. Afterward, clear hazy images were obtained using the atmospheric scattering model. To further enhance the overall brightness and contrast of the dehazed images, white balance correction was performed. Specifically, images were converted into the hue-saturation-value (HSV) color space. Then, the details and contrast of images were enhanced using the proposed adaptive saturation correction and improved contrast enhancement algorithm, as well as Laplacian sharpening. Results and Conclusions Images of typical, actual scenarios were processed using five algorithms: dark channel prior (DCP), maximum reflectance prior (MRP), optimal-scale fusion-based dehazing (OSFD), multiscale fusion – low light image enhancement (MF-LIME), and contrast enhancement and exposure fusion (CEEF). The processing results of these algorithms were those of the proposed algorithm based on multiple indicators. The results indicate that compared to the above novel and excellent algorithms in terms of their optimal indicators, the proposed algorithm exhibited that (1) the average gradients were approximately twice those obtained by CEEF, suggesting elevated image clarity; (2) the average information entropy decreased by approximately 1% compared to that of MRP, implying more information preserved; (3) the standard deviation increased by approximately 6% on average compared to OSFD, representing improved image contrast; (4) the average fog aware density evaluator (FADE) value by approximately 23% compared to CEEF, implying an effective reduction in the haze density. Besides, the proposed new algorithm ran faster. All these demonstrate the superior performance of the new algorithm. Therefore, the proposed algorithm can effectively improve the visual effects and quality of blurred images of underground mining environments in coal mines, exhibiting high utility in engineering.