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• W4292794826 abstract "Image inpainting (a.k.a. image completion) allows us to remove unexpected foreground objects from an observed image and to restore the removed region with background pixels. The performance of image inpainting is improved by auxiliary cues such as edge boundaries and segmentation regions. As a new auxiliary cue, this paper focuses on a depth image that is estimated from an input RGB image by monocular depth estimation. In the depth image, boundaries between different objects (e.g., objects located in different distances) with similar pixel values might be available, while those boundaries are difficult to be detected by edge detection and segmentation. Our proposed method employs those boundaries in the edge and depth images as auxiliary cues. Experiments demonstrate that our proposed method augmented by the depth image outperforms its baseline quantitatively (i.e., 1.17dB and 0.74dB PSNR gains on the Paris-StreetView and Places datasets, respectively) and qualitatively." @default.
• W4292794826 created "2022-08-23" @default.
• W4292794826 creator A5032877617 @default.
• W4292794826 creator A5036204104 @default.
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• W4292794826 date "2022-06-01" @default.
• W4292794826 modified "2023-09-30" @default.
• W4292794826 title "Boundary-aware Image Inpainting with Multiple Auxiliary Cues" @default.
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• W4292794826 doi "https://doi.org/10.1109/cvprw56347.2022.00077" @default.
• W4292794826 hasPublicationYear "2022" @default.
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