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Inductive Guided Filter: Real-Time Deep Matting with Weakly Annotated Masks on Mobile Devices

Recently, significant progress has been achieved in deep image matting. Most of the classical image matting methods are time-consuming and require an ideal trimap which is difficult to attain in practice. An efficient image matting method based on a weakly annotated mask is in demand for mobile applications. In this paper, we propose a novel method called Inductive Guided Filter, which tackles the real-time general image matting task with weakly annotated masks on mobile devices. The Inductive Guided Filter exploits the gradient prior implicit in Guided Filter to reduce the computational burden tremendously in a deep learning manner. The use of Gabor loss is also proposed for complicated textures in image matting. Moreover, we create an image matting dataset MAT-2793 with a variety of foreground objects. Experimental results demonstrate that our proposed method massively reduces running time with robust accuracy.