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In response to the challenge of obtaining clear monitoring images in non-ferrous metal smelters, particularly under complex atmospheric conditions and varying levels of dust and fog concentrations, we have designed an indoor dust and fog image clarification algorithm in this paper. This algorithm utilizes a concentration evaluation to obtain fog weights and construct a refined transmittance in dense fog regions. Transmittance values for non-dense fog regions are calculated using the a priori dark channel and ambient light matrix of the region. The Alpha blending technique is used to fuse ambient light and transmittance matrices of different regions, while bootstrap filtering is utilized to suppress generated noise and preserve image edge information. Finally, the global ambient light and transmittance values are obtained and substituted into the atmospheric scattering model to recover low-illumination dust and fog images. Experimental results demonstrate that the proposed algorithm achieves excellent performance in non-ferrous metal smelting environments by effectively reducing fog concentration in images, improving image illumination, and preserving image edge information.
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