Combination of single image super resolution and digital Inpainting algorithms based on GANS for robust image completion
| Autor: | Viacheslav V. Voronin, Gevorg Karapetyan, Hakob Sarukhanyan, Sparik Hayrapetyan |
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| Rok vydání: | 2017 |
| Předmět: |
Computer Networks and Communications
business.industry Mechanical Engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Inpainting deep learning Energy Engineering and Power Technology super-resolution Superresolution Image (mathematics) Hardware and Architecture Control and Systems Engineering Computer graphics (images) Computer vision lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence Electrical and Electronic Engineering Single image business lcsh:TK1-9971 Mathematics |
| Zdroj: | Serbian Journal of Electrical Engineering, Vol 14, Iss 3, Pp 379-386 (2017) |
| ISSN: | 2217-7183 1451-4869 |
| DOI: | 10.2298/sjee1703379h |
| Popis: | Image inpainting, a technique of completing missing or corrupted image regions in undetected form, is an open problem in digital image processing. Inpainting of large regions using Deep Convolutional Generative Adversarial Nets (DCGAN) is a new and powerful approach. In described approaches the size of generated image and size of input image should be the same. In this paper we propose a new method where the size of input image with corrupted region can be up to 4 times larger than generated image. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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