Integrated image defogging network based on improved atmospheric scattering model and attention feature fusion

During the imaging process, the image will be polluted by noise due to the influence of the earth's atmosphere fog. Noise pollution directly affects the processing of feature extraction, image segmentation, and pattern recognition. Therefore, the purpose of image defogging is to eliminate noise from foggy images to improve image quality. When dealing with uneven fog density in the same image, most of the existing methods are not satisfactory. Integrated image defogging network based on improved atmospheric scattering model and attention feature fusion is proposed. In order to distinguish between the presence of fog with different concentrations in the image. We adopt the concept of attention that has been important in convolutional neural networks (CNN) in recent years. We adopt the attention mechanism to help our network pay more attention to important feature information, treat thick fog and mist differently, and solve the problem of uneven distribution of fog in the image, but the network handles the problem with the same weight. Finally, use a five-layer convolution operation to restore the image. The experimental results show that our method achieves a good defogging effect on the uneven distribution of the fog density of the synthesized foggy image. In the experiment of real natural foggy images, relying on the attention mechanism network to remove the fog, serious patches will appear on the restored images. Our method will not damage the image. Our method can reasonably remove thick fog and mist to obtain a good defogging effect. And our proposed method is robust.