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Refined-mask guided multi-stream blending network
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DOI：10.1007/s11042-023-17793-6.png)
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摘要
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Image composition is a challenging image editing operation that targets the compositing of a new image by combining cropped regions from different images. A common application is background replacement of portrait images. Existing composition methods usually result in undesirable artifacts along the boundaries of pasted regions due to imprecise extraction. To address this problem, we propose a refined-mask guided multi-stream blending network (MGMB-Net), which consists of a multi-stream block (MSB), reconstructing the foreground boundary of composited image, mask-refine blocks (MRBs), providing better location information and refined-mask guided blocks (MGBs), guiding the reconstruction of composited image to eliminate boundary artifacts. MGMB-Net can generate more harmonious and realistic images by fusing multi-stream features and refining input coarse masks. We propose two data-generation methods to construct new datasets on image blending, and two evaluation metrics, PSNR (Peak signal-to-noise ratio)-Sobel and PSNR-Boundary, to decouple the performance of blending and harmonization. Compared with state-of-the-art methods, our method achieves the best performance on portrait images without additional refinements or prior information. Our code and model are available at https://github.com/Wekect/MGMB-Net.
Keyword:
Image blending
Image composition
Boundary artifacts
Mask refine
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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