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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Journal ArticleDOI
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images
TL;DR: Tang et al. as mentioned in this paper proposed a novel time-dependent encoding method called the Gramian Angular Summation Field (GASF) that converts the problem of traffic time-series data generation into that of image generation.
Book ChapterDOI
ChaLearn Looking at People: Inpainting and denoising challenges
Sergio Escalera,Marti Soler,Stéphane Ayache,Umut Güçlü,Jun Wan,Meysam Madadi,Xavier Baró,Hugo Jair Escalante,Isabelle Guyon +8 more
TL;DR: This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018.
Proceedings ArticleDOI
Perceptual Artifacts Localization for Inpainting
Lingzhi Zhang,Yuqian Zhou,Connelly Barnes,Sohrab Amirghodsi,Zhe L. Lin,E. Shechtman,Jianbo Shi +6 more
TL;DR: A new learning task of automatic segmentation of inpainting perceptual artifacts is proposed, and a new interpretable evaluation metric called Perceptual Artifact Ratio (PAR), which is the ratio of objectionable inpainted regions to the entire inpainted area is proposed.
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Blind Image Deconvolution using Pretrained Generative Priors
TL;DR: This paper proposed an alternating gradient descent scheme operating in the latent lower-dimensional space of each of the pretrained generative models to deblur, which showed excellent deblurring results even under large blurs and heavy noise.
Proceedings ArticleDOI
Semantic Image Completion and Enhancement using Deep Learning
TL;DR: Experimental outcomes show that the proposed approach improves the Peak Signal to Noise ratio and Structural Similarity Index values by 2.45% and 4% respectively, when compared to the recently reported data.
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