Aggregated Contextual Transformations for High-Resolution Image Inpainting

Image inpainting that completes large free-form missing regions in images is a promising yet challenging task. State-of-the-art approaches have achieved significant progress by taking advantage of generative adversarial networks (GAN). However, these approaches can suffer from generating distorted s...

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Vydané v:	IEEE transactions on visualization and computer graphics Ročník 29; číslo 7; s. 3266 - 3280
Hlavní autori:	Zeng, Yanhong, Fu, Jianlong, Chao, Hongyang, Guo, Baining
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.07.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Cognition Context Convolution Discriminators Filling Free form Generative adversarial networks generative adversarial networks (GAN) Generators High resolution Image enhancement image inpainting Image resolution Image synthesis object removal Reasoning State-of-the-art reviews Synthesis Task analysis Texture Training
ISSN:	1077-2626, 1941-0506, 1941-0506
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