Abstract:

Image editing technology, which is widely used in the post-production of film and television and in daily life, is based on image matting. In this study, an image matting network based on deep learning which estimates the value of each pixel by inputting the original image and trimap is proposed. Based on the original down- and up-sampling network and to address the problem of slow network convergence caused by the large difference between matting dataset pictures, batch normalisation (BN) is applied after each convolution layer in this study. In the normalisation layer, the input data are normalised to speed up the convergence of the model. This enables the update direction of the parameters to be more consistent with the overall characteristics of the dataset. Because the edge of the object should be carefully considered in the matting task, a deformable convolution layer is used instead of the custom convolution layer. The deformable convolution layer can adaptively learn the shape of the convolution kernel according to different input data, effectively expand the range of the receptive field, and improve the prediction effect in detailed image parts.

Key words: deep learning, image matting, semantic segmentation, prediction