Conventional Retinex defogging algorithm does not consider the depth-of-field information of fogged images and restores the entire image at the same scale, resulting in local color distortion and loss of image details. An adaptive Retinex image defogging algorithm that uses depth-of-field information to remedy these disadvantages is proposed. As fog concentration and depth of field are closely related, the depth-of-field of foggy images is estimated using the BTS depth learning model. The average gradient of the image is considered the optimal evaluation standard, following which the foggy image is processed in blocks. The Retinex enhancement adopts different Gaussian filtering scales, and the optimal Gaussian filtering scale as well as the corresponding average depth-of-field are estimated. The parameter models of depth-of-field estimation and Gaussian filtering scale are obtained via the gradient descent method and applied to the single-scale Retinex defogging algorithm to process the fogged images in blocks. Finally, by calculating the mean value and mean square deviation, and defining a parameter to control the image dynamics, we can realize adaptive contrast stretching without color deviation. Moreover, bilinear interpolation mapping can also be applied to increase the continuity of the image block edges and obtain an enhanced defogging image. Experimental results show that the standard deviation, average brightness, information entropy, square gradient, and other evaluation indicators after defogging using the proposed algorithm are better than those of the contrast algorithm. In practice, the defogged image has higher contrast, the image details remain intact, and excessive enhancement is suppressed. The adaptive Retinex image defogging algorithm based on depth-of-field information proposed in this paper has a high degree of adaptation and can effectively retain image details with natural color that conforms to the characteristics of human vision, making it superior to the conventional Retinex defogging algorithm.