Image quilting for texture synthesis and transfer

By Alexei A. Efros, William T. Freeman. 2001

2,923 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Generative Models

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In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

In Generative Models

Citations in the corpus

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks (2021, methodology): “Traditional methods (Ballester et al., 2001; Barnes et al., 2009; Darabi et al., 2012; Efros & Freeman, 2001; Efros & Leung, 1999) utilize only low-level features and fail to generate semantically consistent contents.”
Free-Form Image Inpainting With Gated Convolution (2018, methodology): “…exist: patch-based ones with low-level features and deep generative models with convolutional neural networks. former approach [Barnes et al. 2009; Efros and Freeman 2001; Efros and Leung 1999] can synthesize plausible stationary textures, but usually make critical failures in non-stationary…”
Image-to-Image Translation with Conditional Adversarial Networks (2016, background): “This connection was previously explored in [35], and is also the common assumption in models of texture [15, 19] and style [14, 23, 20, 34].”
Rewriting a Deep Generative Model (2020, background): “Common operations include color transfer [65,53], image deformation [67,75], object cloning [62,11], and patch-based image synthesis [20,5,29].”
A Learned Representation For Artistic Style (2016, positive): “Efros & Freeman (2001) and Liang et al. (2001) extend this idea to “growing” textures one patch at a time, and Efros & Freeman (2001) uses the approach to implement “texture transfer”, i.e. transfering the texture of an object onto another one.”
Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization (2017, methodology): “The problem of style transfer has its origin from non-photo-realistic rendering [28], and is closely related to texture synthesis and transfer [13, 12, 14].”
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks (2016, background): “Consequently, the ﬁdelity of details is good, but additional guidance is required if non-trivial global structure should be reproduced [6,12,1,8,21].”
Countering Adversarial Images using Input Transformations (2018, background): “Image quilting (Efros & Freeman, 2001) is a non-parametric technique that synthesizes images by piecing together small patches that are taken from a database of image patches.”
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis (2016, methodology): “These can be either explicitly provided by the user [1, 2, 5, 7, 12, 16, 27], or learned from non-local image statistics [11, 18, 30].”
View Synthesis by Appearance Flow (2016, neutral): “In texture synthesis [28,29], the synthesized image is pieced together by combining samples of the input texture image in a visually consistent way, whereas for texture transfer [30,31], an additional constraint aims to make the overall result also mimic a secondary “source” image.”
Generative Image Inpainting with Contextual Attention (2018, background): “Traditional diffusion or patch-based approaches such as [2, 4, 9, 10] typically use variational algorithms or patch similarity to propagate information from the background regions to the holes.”
Image Inpainting for Irregular Holes Using Partial Convolutions (2018, methodology): “Patch-based methods such as [5, 15] operate by searching for relevant patches from the image’s non-hole regions or other source images in an iterative fashion.”
Exploring the structure of a real-time, arbitrary neural artistic stylization network (2017, background): “(2001) [4] demonstrated that one may transfer a texture to an arbitrary photograph to confer it with the stylism of a drawing.”
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].”
Fast Patch-based Style Transfer of Arbitrary Style (2016, background): “Early works deﬁne style using similarity measures or local statistics based on the pixel values [7, 8, 13, 14, 20, 24].”
Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation (2011, methodology): “As exemplars, patch-based methods are currently the focus of attention of the computer vision community in various domains such as texture synthesis (Efros and Freeman, 2001), in-painting (Criminisi et al., 2004), restoration (Buades et al., 2005), and single-frame super resolution (Protter et al.,…”
PEPSI : Fast Image Inpainting With Parallel Decoding Network (2019, methodology): “Image inpainting techniques have been widely re-searched [1–3, 5, 6, 10, 13, 16, 17, 19, 21, 25, 26, 28] for removing an unwanted object or synthesizing missing parts of an image in various applications such as photo editing, image-based rendering, and computational photography [15, 20, 28].”