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Due to the atmospheric scattering phenomenon in fog weather, the current monitoring video image defogging method cannot estimate the fog density of the image. This paper proposes a real-time defogging algorithm for single images of IoTs surveillance video based on maximum a posteriori (MAP). Under the condition of single image sequence, the posterior probability of the high-resolution single image is set to the maximum, which improves the MAP design super-resolution image reconstruction. This paper introduces fuzzy classification to calculate atmospheric light intensity, and obtains a single image of IoTs surveillance video by the atmospheric dissipation function. The improved algorithm has the largest signal-to-noise ratio after defogging, and the maximum value is as high as 40.99 dB. The average time for defogging of 7 experimental surveillance video images is only 2.22 s, and the real-time performance is better. It can be concluded that the proposed algorithm has excellent defogging performance and strong applicability.