dugMatting: Decomposed-Uncertainty-Guided Matting
Jiawei Wu Changqing Zhang Zuoyong Li Huazhu Fu Xi Peng Joey Tianyi Zhou
Cutting out an object and estimating its opacity mask, known as image
matting, is a key task in image and video editing. Due to the highly ill-posed
issue, additional inputs, typically user-defined trimaps or scribbles, are
usually needed to reduce the uncertainty. Although effective, it is either time
consuming or only suitable for experienced users who know where to place the
strokes. In this work, we propose a decomposed-uncertainty-guided matting
(dugMatting) algorithm, which explores the explicitly decomposed uncertainties
to efficiently and effectively improve the results. Basing on the
characteristic of these uncertainties, the epistemic uncertainty is reduced in
the process of guiding interaction (which introduces prior knowledge), while
the aleatoric uncertainty is reduced in modeling data distribution (which
introduces statistics for both data and possible noise). The proposed matting
framework relieves the requirement for users to determine the interaction areas
by using simple and efficient labeling. Extensively quantitative and
qualitative results validate that the proposed method significantly improves
the original matting algorithms in terms of both efficiency and efficacy.