Refined image colorization using capsule generative adversarial networks

Raggi Hosni; Walid Hussein

doi:10.1117/12.2556645

31 January 2020 Refined image colorization using capsule generative adversarial networks

Raggi Hosni, Walid Hussein

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114332R (2020) https://doi.org/10.1117/12.2556645
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Automatic image colorization has increasingly become a heavily researched topic over the last decade. It has become of interest for many application areas including colorization of black and white movies, historical images, surveillance feeds and generally old image restoration. Recent state-of-the-art methods utilize Deep Convolutional Generative Adversarial Networks (DCGAN) for the colorization process. However, with the introduction of capsule networks, many flaws of the convolutional neural networks began to surface. In this paper, the convolutional network layers inside the discriminator of a DCGAN will be replaced with capsule network layers. Further studies are employed to show how capsule networks as a discriminator in a DCGAN perform in the image colorization task.

Citation Download Citation

Raggi Hosni and Walid Hussein "Refined image colorization using capsule generative adversarial networks", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114332R (31 January 2020); https://doi.org/10.1117/12.2556645

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KEYWORDS

Gallium nitride

Image quality

Neurons

Nose

Convolutional neural networks

Data modeling

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