In order to solve the problem of dark color and incomplete defogging after image defogging, an image defogging algorithm based on multi-scale concat convolutional neural network is proposed in this paper. Taking the foggy image as the input, the shallow layer information of the image is extracted from the single scale convolution layer through the preprocessing module, and then the multi-scale mapping module is designed to realize the depth feature learning and the fusion of the deep and shallow layer features. The deconvolution module is used to restore the image size, and the coarse transmittance map corresponding to the foggy image is obtained through the convolution operation. Finally, the haze free image is restored according to the atmospheric scattering model. The experimental results show that the proposed method is superior to other algorithms in both synthetic and natural foggy images, and the peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) can reach 29.238 and 0.950, respectively. The proposed algorithm can effectively avoid the dark color and distortion of the image, improve the image defogging performance and show good visual effect.