Proceedings ArticleDOI
Image analogies
Aaron Hertzmann,Charles E. Jacobs,Nuria Oliver,Brian Curless,David Salesin +4 more
- pp 327-340
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TLDR
This paper describes a new framework for processing images by example, called “image analogies,” based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis.Abstract:
This paper describes a new framework for processing images by example, called “image analogies.” The framework involves two stages: a design phase, in which a pair of images, with one image purported to be a “filtered” version of the other, is presented as “training data”; and an application phase, in which the learned filter is applied to some new target image in order to create an “analogous” filtered result. Image analogies are based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis. By choosing different types of source image pairs as input, the framework supports a wide variety of “image filter” effects, including traditional image filters, such as blurring or embossing; improved texture synthesis, in which some textures are synthesized with higher quality than by previous approaches; super-resolution, in which a higher-resolution image is inferred from a low-resolution source; texture transfer, in which images are “texturized” with some arbitrary source texture; artistic filters, in which various drawing and painting styles are synthesized based on scanned real-world examples; and texture-by-numbers, in which realistic scenes, composed of a variety of textures, are created using a simple painting interface.read more
Citations
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Proceedings ArticleDOI
Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
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Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
TL;DR: CycleGAN as discussed by the authors learns a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss.
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Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional Adversarial Network (CA) as discussed by the authors is a general-purpose solution to image-to-image translation problems, which can be used to synthesize photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
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Image Style Transfer Using Convolutional Neural Networks
TL;DR: A Neural Algorithm of Artistic Style is introduced that can separate and recombine the image content and style of natural images and provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
TL;DR: This work presents an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples, and introduces a cycle consistency loss to push F(G(X)) ≈ X (and vice versa).
References
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Metaphors We Live By
George Lakoff,Mark Johnson +1 more
TL;DR: Lakoff and Johnson as mentioned in this paper suggest that these basic metaphors not only affect the way we communicate ideas, but actually structure our perceptions and understandings from the beginning, and they offer an intriguing and surprising guide to some of the most common metaphors and what they can tell us about the human mind.
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Metaphors We Live by
TL;DR: Lakoff and Johnson as discussed by the authors present a very attractive book for linguists to read, which is written in a direct and accessible style; while it introduces and uses a number of new terms, for the most part it is free of jargon.
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Allen Gersho,Robert M. Gray +1 more
TL;DR: The author explains the design and implementation of the Levinson-Durbin Algorithm, which automates the very labor-intensive and therefore time-heavy and expensive process of designing and implementing a Quantizer.