Semantic Image Inpainting with Deep Generative Models
Raymond A. Yeh,Chen Chen,Teck Yian Lim,Alexander G. Schwing,Alexander G. Schwing,Mark Hasegawa-Johnson,Minh N. Do +6 more
- pp 6882-6890
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TLDR
A novel method for semantic image inpainting, which generates the missing content by conditioning on the available data, and successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.Abstract:
Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data. Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context. In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data. Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses. This encoding is then passed through the generative model to infer the missing content. In our method, inference is possible irrespective of how the missing content is structured, while the state-of-the-art learning based method requires specific information about the holes in the training phase. Experiments on three datasets show that our method successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.read more
Citations
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Proceedings ArticleDOI
Image Processing Using Multi-Code GAN Prior
Jinjin Gu,Yujun Shen,Bolei Zhou +2 more
TL;DR: Mao et al. as discussed by the authors employed multiple latent codes to generate multiple feature maps at some intermediate layer of the generator, then compose them with adaptive channel importance to recover the input image.
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Patch-Based Image Inpainting with Generative Adversarial Networks.
Ugur Demir,Gozde Unal +1 more
TL;DR: The proposed PGGAN method includes a discriminator network that combines a global GAN (G-GAN) architecture with a patchGAN approach that feeds the generator network in order to capture both local continuity of image texture and pervasive global features in images.
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Deep Generative Adversarial Networks for Compressed Sensing Automates MRI.
Morteza Mardani,Enhao Gong,Joseph Y. Cheng,Shreyas S. Vasanawala,Greg Zaharchuk,Marcus T. Alley,Neil Thakur,Song Han,William J. Dally,John M. Pauly,Lei Xing +10 more
TL;DR: A novel CS framework that permeates benefits from generative adversarial networks (GAN) to train a (low-dimensional) manifold of diagnostic-quality MR images from historical patients, which offers reconstruction under a few milliseconds, two orders of magnitude faster than state-of-the-art CS-MRI schemes.
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Digital Signal Modulation Classification With Data Augmentation Using Generative Adversarial Nets in Cognitive Radio Networks
TL;DR: This work proposes a smart approach to programmatic data augmentation method by using the auxiliary classifier generative adversarial networks (ACGANs) and shows that it can gain 0.1~6% increase in the classification accuracy in the ACGAN-based data set.
Book ChapterDOI
Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations
TL;DR: Li et al. as mentioned in this paper proposed a mutual encoder-decoder CNN for joint recovery of both structures and textures. But, the CNN features of each encoder are learned to capture either missing structures or textures without considering them as a whole.
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