Papers › Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation

Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation

20 Sep 2019ICCV 2019 10arXiv:1909.09725archive 2025-07-28

Qiqi Hou, Feng Liu

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.

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Code

hqqxyy/Context-Aware-Matting officialmentioned in papermentioned on GitHubtf report

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Tasks

Data Augmentation Decoder Image Matting

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image Matting	Composition-1K	Context-Aware Matting	Conn	33.2	#11 of 13	Archive leaderboard	report
Image Matting	Composition-1K	Context-Aware Matting	Grad	17.3	#11 of 13	Archive leaderboard	report
Image Matting	Composition-1K	Context-Aware Matting	MSE	8.2	#11 of 13	Archive leaderboard	report
Image Matting	Composition-1K	Context-Aware Matting	SAD	35.8	#11 of 13	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Convolution Dense Connections Depthwise Convolution Depthwise Separable Convolution Max Pooling Pointwise Convolution ReLU Residual Connection

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