Deep Photo Style Transfer
Fujun Luan,Sylvain Paris,Eli Shechtman,Kavita Bala +3 more
- pp 6997-7005
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TLDR
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style and constrain the transformation from the input to the output to be locally affine in colorspace.Abstract:
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network. However, as is, this approach is not suitable for photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. Our contribution is to constrain the transformation from the input to the output to be locally affine in colorspace, and to express this constraint as a custom fully differentiable energy term. We show that this approach successfully suppresses distortion and yields satisfying photorealistic style transfers in a broad variety of scenarios, including transfer of the time of day, weather, season, and artistic edits.read more
Citations
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Data augmentation for improving deep learning in image classification problem
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Contrastive Learning for Unpaired Image-to-Image Translation
TL;DR: The framework enables one-sided translation in the unpaired image-to-image translation setting, while improving quality and reducing training time, and can be extended to the training setting where each "domain" is only a single image.
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Neural Style Transfer: A Review
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Neural Style Transfer: A Review
TL;DR: A taxonomy of current algorithms in the field of NST is proposed and several evaluation methods are presented and compared to compare different NST algorithms both qualitatively and quantitatively.
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Shift-Net: Image Inpainting via Deep Feature Rearrangement
TL;DR: A special shift-connection layer to the U-Net architecture, namely Shift-Net, is introduced for filling in missing regions of any shape with sharp structures and fine-detailed textures and an end-to-end learning algorithm is further developed to train the Shift- net.
References
More filters
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings ArticleDOI
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.
Journal ArticleDOI
Color transfer between images
TL;DR: This work uses a simple statistical analysis to impose one image's color characteristics on another by choosing an appropriate source image and applying its characteristic to another image.
Journal ArticleDOI
A Closed-Form Solution to Natural Image Matting
TL;DR: A closed-form solution to natural image matting that allows us to find the globally optimal alpha matte by solving a sparse linear system of equations and predicts the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms.
Proceedings ArticleDOI
Image analogies
TL;DR: 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.