ing at solving the problems of color and contrast distortions in traditional dehazing algorithms, we propose an image dehazing algorithm based on multi-scale fusion and adversarial training. The multi-scale feature extraction block is used to extract haze-relevant features from different scales, and the residual-and-densely-connected block is used to realize the interaction of image features and avoid gradient vanishing. Because the algorithm is not based on the atmospheric scattering model and directly fuses the shallow and deep features of the image in the multi-scale manner, so it overcomes the inaccuracy of physical model. The dehazing network is trained via the generative adversarial mechanism, the generator uses the multi-scale feature extraction block and the residual-and-densely-connected block to estimate the haze-free image, and the discriminator consisting of two sub-networks with different receptive fields carries out the adversarial training. Comparison experiments on the RESIDE (Realistic single image dehazing) dataset show that the dehazed images generated by the proposed algorithm are more visually pleasant than those by other algorithms in terms of full-reference and no-reference visual quality indicators.