Fragment-based image completion

By Iddo Drori, Daniel Cohen-Or, Yehezkel Yeshurun. 2003

735 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Free-Form Image Inpainting With Gated Convolution (2018, methodology): “To improve image inpainting, different user guidance is explored including dots or lines [Ashikhmin 2001; Barnes et al. 2009; Drori et al. 2003; Sun et al. 2005], structures [Huang et al. 2014], transformation or distortion information [Huang et al. 2013; Pavi´c et al. 2006] and image exemplars…”
High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis (2016, background): “The first group of approaches relies on texture synthesis techniques, which fills in the hole by extending textures from surrounding regions [14, 13, 27, 26, 6, 12, 40, 41, 23, 24, 2].”
Image Inpainting : Overview and Recent Advances (2014, methodology): “The missing regions are iteratively approximated using some guidance from coarse to fine levels [24].”
Guided Image Inpainting: Replacing an Image Region by Pulling Content From Another Image (2018, methodology): “Automated image inpainting is an established computer vision problem with many valuable applications [16, 37, 6, 9, 38, 27, 45].”
Shift-Net: Image Inpainting via Deep Feature Rearrangement (2018, neutral): “In exemplar-based inpainting [2, 3, 5, 7, 9, 17, 18, 22, 23, 25, 32, 37, 39, 43, 44, 46], the completion is conducted from the exterior to the interior of the missing part by searching and copying best matching patches from the known region.”
Example Based 3D Reconstruction from Single 2D Images (2006, methodology): “We note in particular, the connection between our method, and Image Analogies [13].”
Transformation guided image completion (2013, methodology): “Most automatic techniques (such as [1, 7, 6, 22, 14, 8]) are able to handle a specific set of structural conditions, but mostly fine to mid-level textures.”
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting (2020, background): “Patch-based algorithms like [9, 10, 11, 29] progressively fill pixels in the hole by searching the image patches from background regions that are the most similar to the pixels along the hole boundaries.”
Semantic Image Inpainting with Progressive Generative Networks (2018, background): “Although non-semantic image inpainting approaches have been studied for decades [2, 5– 7, 9, 17, 25, 27, 31], they cannot generate semantically meaningful content, and thus are not suitable to handle the semantic image inpainting task where large proportions of areas are corrupted in images.”
Mask-Specific Inpainting with Deep Neural Networks (2014, methodology): “Many successful methods (some based on texture synthesis [8]) have been proposed in the past, for instance [7, 17, 5, 26, 13] and references therein.”
Interactive image completion with perspective correction (2006, methodology): “Other previous completion techniques take user-defined points of interest [12] into account to control the propagation direction when completing rotationally symmetric”