168 research outputs found
Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation
Natural image matting is an important problem in computer vision and
graphics. It is an ill-posed problem when only an input image is available
without any external information. While the recent deep learning approaches
have shown promising results, they only estimate the alpha matte. This paper
presents a context-aware natural image matting method for simultaneous
foreground and alpha matte estimation. Our method employs two encoder networks
to extract essential information for matting. Particularly, we use a matting
encoder to learn local features and a context encoder to obtain more global
context information. We concatenate the outputs from these two encoders and
feed them into decoder networks to simultaneously estimate the foreground and
alpha matte. To train this whole deep neural network, we employ both the
standard Laplacian loss and the feature loss: the former helps to achieve high
numerical performance while the latter leads to more perceptually plausible
results. We also report several data augmentation strategies that greatly
improve the network's generalization performance. Our qualitative and
quantitative experiments show that our method enables high-quality matting for
a single natural image. Our inference codes and models have been made publicly
available at https://github.com/hqqxyy/Context-Aware-Matting.Comment: This is the camera ready version of ICCV2019 pape
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