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End-to-End Conditional GAN-based Architectures for Image Colourisation

Authors: 

Marc Górriz Blanch, Marta Mrak, Alan Smeaton, Noel O'Connor

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
In this work recent advances in conditional adversarial networks are investigated to develop an end-to-end architecture based on Convolutional Neural Networks (CNNs) to directly map realistic colours to an input greyscale image. Observing that existing colourisation methods sometimes exhibit a lack of colourfulness, this paper proposes a method to improve colourisation results. In particular, the method uses Generative Adversarial Neural Networks (GANs) and focuses on improvement of training stability to enable better generalisation in large multi-class image datasets. Additionally, the integration of instance and batch normalisation layers in both generator and discriminator is introduced to the popular U-Net architecture, boosting the network capabilities to generalise the style changes of the content. The method has been tested using the ILSVRC 2012 dataset, achieving improved automatic colourisation results compared to other methods based on GANs.
Conference Name: 
Proceedings of IEEE 21st International Workshop on Multimedia Signal Processing
Proceedings: 
Proceedings of Proceedings of IEEE 21st International Workshop on Multimedia Signal Processing
Digital Object Identifer (DOI): 
10.1109/MMSP.2019.8901712
Publication Date: 
05/09/2019
Conference Location: 
Malaysia
Research Group: 
Institution: 
Dublin City University (DCU)
Open access repository: 
Yes