To solve the problem of image clarity and contrast degradation in fog scene image restoration, a single image defogging algorithm based on residual learning and guided filtering was proposed. A residual network was first constructed by using foggy images and corresponding clear images. Multi-scale convolution was then used to extract more detailed haze features. Taking advantage of the anisotropy of the guided filter, the algorithm then obtained a clearer fog-free image after the residual network was filtered to maintain image edge characteristics. Experiments produced the following results as compared with the dark channel prior, CAP, super-resolution convolutionalneuralnetwork, DehazeNet, and multi-scale convolutional neural network algorithms.On synthetic foggy images, the peak signal-to-noise ratio reacheda maximum of 27.840 3 dB, the structural similarity index measurereacheda maximum of 0.979 6, and the runtime on natural foggy images was as low as 0.4 s.In addition, the subjective and objective evaluations proved to be better than those of the other comparison algorithms.Thus, the proposed defogging algorithm not only produces a better defogging effectbut is also faster, there by offering a greater practical valuefor defogging applications than the other algorithms.