Abstract Aiming at the problem that the image is degraded by a large number of suspended particles such as soot and water mist generated in the smelting workshop, an dust removal algorithm for the atmospheric scattering model image of the smelting workshop is proposed. In order to better estimate the real atmospheric light value, the algorithm in this paper obtains the initial atmospheric light value by simple linear iterative clustering segmentation algorithm, and uses fast guided filtering to refine the initial atmospheric light value. At the same time, the adaptive gamma function is used to correct the atmospheric light and the original soot image, and the final atmospheric light and the optimized soot image are obtained respectively. The transmittance is estimated by the optimized color attenuation prior model. Finally, the smoke-free image is restored according to the atmospheric scattering model. The experimental results show that the algorithm can effectively reduce the smoke concentration in the image and reduce the loss of image details. The mean square error is reduced by 66.2% on average, the peak signal-to-noise ratio is increased by 30.5% on average, and the structural similarity is increased by 48.6% on average.
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2023, Vol. 24 
