The traditional defogging algorithm based on dark channel prior can not effectively remove the fog points in the sudden change of field depth, and it is easy to cause halo effect at the boundary. In this paper, an adaptive super-pixel defogging algorithm based on dark channel prior is proposed. Firstly, in the acquisition process of the dark channel, an adaptive method is introduced to determine whether there are multiple depth-of-field objects in the neighborhood of the current pixel. If there are only the same depth-of-field objects, the dark channel of the pixel can be obtained directly. If there are multiple depth-of-field objects, a super-pixel segmentation algorithm is introduced to distinguish different depth-of-field objects, so as to reduce the influence of depth-of-field change on dark channel acquisition and to obtain a more accurate dark channel. Then the rough transmittance is estimated, and the transmittance is refined according to the context constraints. Finally, the defogging image is solved by the inverse process of image degradation. The experimental results show that compared with dark channel prior (DCP), a fast image defogging algorithm based on edge-maximum filter (EMDCP), single image dehazing based on adaptive dark channel prior (ADCP) and efficient image dehazing with boundary constraint and contextual regularization (BCCR) algorithms, the comprehensive assessment criteria index of the proposed method is improved by 10%, which can suppress halo effect and improve the visual effect of foggy images.