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Image matting technology is an important topic in computer vision and has been used in a wide variety of applications. In particular, portrait matting is widely used in film, advertising, short video and other areas. Matting is not a simple image segmentation problem, a good matting algorithm can deal with hair and other details accurately. In this paper, we combine the fully convolutional dense network named Tiramisu with the generative adversarial network(GAN). Through Tiramisu, we can obtain a coarse alpha channel and the RGB channels with chromatic aberration. Next, we use the structure of GAN to refine the details, the network needs to adjust the boundary of alpha channel and the color values of the other three channels. However, the instability is GAN's shortcoming. In order to solve this weakness, Wassertein distance is introduced into the loss function of GAN. It completely solves the problem of unstable training of GAN, and basically solves mode collapsing problem. We compare the proposed approach with several state-of-the-art image matting methods on synthetic datasets. The main difference between our method and other methods is that we generate an RGBA image rather than only an alpha channel. It makes full use of the advantages of GAN in the field of image generation. The result shows our model outperforms most of its competitors in the image quality, and it is superior than all of competitors in real-time performance. |
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Keywords:Portrait Matting, Fully Convolutional Dense Network, Generative Adversarial Network, Image Processing. |
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