In order to solve the problems of high model complexity and large parameters in image dehazing using convolutional neural network, a light image dehazing network (LDNet) is proposed. Firstly, the expression formula of atmospheric scattering model is improved to eliminate fog noise directly and reduce the cumulative error of intermediate variable estimation. Secondly, a reverse residual network module with attention mechanism (RNAM) is designed, which can extract image features at multiple scales, pay attention to the important semantic information in the image, and solve the problems of large network parameters and high complexity. Finally, L1 smoothing loss function and MS-SSIM loss function are used as joint loss functions to minimize the distance between the restored image and the real image. The experimental results show that the proposed algorithm is superior to other contrast algorithms in terms of SSIM and PSNR on the composite data set of reside, and can achieve good dehazing effect in both synthetic images and real scenes, and the method has the characteristics of less parameters and fast operation.