For color distortion and incomplete dehazing caused by inaccurate media transmittance in the image dehazing algorithm, an image dehazing algorithm with an improved residual neural network is proposed. First, a parallel multi-scale convolutional layer is adopted to extract the characteristics of the haze image. Then the media transmittance is learned by introducing the residual network of the depthwise separable convolutional layer and refined by the weighted guided filter. Finally, according to the atmospheric scattering model, a clear image without hazy is obtained. Experimental results show that compared with other dehazing algorithms, the proposed algorithm improves peak signal to noise ratio (PSNR) and structural similarity (SSIM) indicators, and the dehazing image also performs well in subjective vision.