Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation
This addresses the need for more accurate and efficient image matting in computer vision and graphics, though it is incremental as it builds on existing deep learning approaches.
The paper tackles the ill-posed problem of natural image matting by proposing a context-aware method that simultaneously estimates both the foreground and alpha matte, achieving high-quality results as shown in qualitative and quantitative experiments.
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.