Context-aware semantic inpainting
Authors: Li, H., Li, G., Lin, L., Yu, H. and Yu, Y.
Journal: IEEE Transactions on Cybernetics
Volume: 49
Issue: 12
Pages: 4398-4411
eISSN: 2168-2275
ISSN: 2168-2267
DOI: 10.1109/TCYB.2018.2865036
Abstract:In recent times, image inpainting has witnessed rapid progress due to the generative adversarial networks (GANs) that are able to synthesize realistic contents. However, most existing GAN-based methods for semantic inpainting apply an auto-encoder architecture with a fully connected layer, which cannot accurately maintain spatial information. In addition, the discriminator in existing GANs struggles to comprehend high-level semantics within the image context and yields semantically consistent content. Existing evaluation criteria are biased toward blurry results and cannot well characterize edge preservation and visual authenticity in the inpainting results. In this paper, we propose an improved GAN to overcome the aforementioned limitations. Our proposed GAN-based framework consists of a fully convolutional design for the generator which helps to better preserve spatial structures and a joint loss function with a revised perceptual loss to capture high-level semantics in the context. Furthermore, we also introduce two novel measures to better assess the quality of image inpainting results. The experimental results demonstrate that our method outperforms the state-of-the-art under a wide range of criteria.
https://eprints.bournemouth.ac.uk/31428/
Source: Scopus
Context-Aware Semantic Inpainting.
Authors: Li, H., Li, G., Lin, L., Yu, H. and Yu, Y.
Journal: IEEE Trans Cybern
Volume: 49
Issue: 12
Pages: 4398-4411
eISSN: 2168-2275
DOI: 10.1109/TCYB.2018.2865036
Abstract:In recent times, image inpainting has witnessed rapid progress due to the generative adversarial networks (GANs) that are able to synthesize realistic contents. However, most existing GAN-based methods for semantic inpainting apply an auto-encoder architecture with a fully connected layer, which cannot accurately maintain spatial information. In addition, the discriminator in existing GANs struggles to comprehend high-level semantics within the image context and yields semantically consistent content. Existing evaluation criteria are biased toward blurry results and cannot well characterize edge preservation and visual authenticity in the inpainting results. In this paper, we propose an improved GAN to overcome the aforementioned limitations. Our proposed GAN-based framework consists of a fully convolutional design for the generator which helps to better preserve spatial structures and a joint loss function with a revised perceptual loss to capture high-level semantics in the context. Furthermore, we also introduce two novel measures to better assess the quality of image inpainting results. The experimental results demonstrate that our method outperforms the state-of-the-art under a wide range of criteria.
https://eprints.bournemouth.ac.uk/31428/
Source: PubMed
Context-Aware Semantic Inpainting.
Authors: Li, H., Li, G., Lin, L., Yu, H. and Yu, Y.
Journal: IEEE transactions on cybernetics
Volume: 49
Issue: 12
Pages: 4398-4411
eISSN: 2168-2275
ISSN: 2168-2267
DOI: 10.1109/tcyb.2018.2865036
Abstract:In recent times, image inpainting has witnessed rapid progress due to the generative adversarial networks (GANs) that are able to synthesize realistic contents. However, most existing GAN-based methods for semantic inpainting apply an auto-encoder architecture with a fully connected layer, which cannot accurately maintain spatial information. In addition, the discriminator in existing GANs struggles to comprehend high-level semantics within the image context and yields semantically consistent content. Existing evaluation criteria are biased toward blurry results and cannot well characterize edge preservation and visual authenticity in the inpainting results. In this paper, we propose an improved GAN to overcome the aforementioned limitations. Our proposed GAN-based framework consists of a fully convolutional design for the generator which helps to better preserve spatial structures and a joint loss function with a revised perceptual loss to capture high-level semantics in the context. Furthermore, we also introduce two novel measures to better assess the quality of image inpainting results. The experimental results demonstrate that our method outperforms the state-of-the-art under a wide range of criteria.
https://eprints.bournemouth.ac.uk/31428/
Source: Europe PubMed Central
Context-Aware Semantic Inpainting
Authors: Li, H., Li, G., Lin, L., Yu, H. and Yu, Y.
Journal: IEEE Transactions on Cybernetics
Volume: 49
Issue: 12
Pages: 4398-4411
ISSN: 2168-2267
Abstract:IEEE In recent times, image inpainting has witnessed rapid progress due to the generative adversarial networks (GANs) that are able to synthesize realistic contents. However, most existing GAN-based methods for semantic inpainting apply an auto-encoder architecture with a fully connected layer, which cannot accurately maintain spatial information. In addition, the discriminator in existing GANs struggles to comprehend high-level semantics within the image context and yields semantically consistent content. Existing evaluation criteria are biased toward blurry results and cannot well characterize edge preservation and visual authenticity in the inpainting results. In this paper, we propose an improved GAN to overcome the aforementioned limitations. Our proposed GAN-based framework consists of a fully convolutional design for the generator which helps to better preserve spatial structures and a joint loss function with a revised perceptual loss to capture high-level semantics in the context. Furthermore, we also introduce two novel measures to better assess the quality of image inpainting results. The experimental results demonstrate that our method outperforms the state-of-the-art under a wide range of criteria.
https://eprints.bournemouth.ac.uk/31428/
Source: BURO EPrints