1 research outputs found
Perceptual Conditional Generative Adversarial Networks for End-to-End Image Colourization
Colours are everywhere. They embody a significant part of human visual
perception. In this paper, we explore the paradigm of hallucinating colours
from a given gray-scale image. The problem of colourization has been dealt in
previous literature but mostly in a supervised manner involving
user-interference. With the emergence of Deep Learning methods numerous tasks
related to computer vision and pattern recognition have been automatized and
carried in an end-to-end fashion due to the availability of large data-sets and
high-power computing systems. We investigate and build upon the recent success
of Conditional Generative Adversarial Networks (cGANs) for Image-to-Image
translations. In addition to using the training scheme in the basic cGAN, we
propose an encoder-decoder generator network which utilizes the class-specific
cross-entropy loss as well as the perceptual loss in addition to the original
objective function of cGAN. We train our model on a large-scale dataset and
present illustrative qualitative and quantitative analysis of our results. Our
results vividly display the versatility and proficiency of our methods through
life-like colourization outcomes.Comment: 16 pages, 8 figures, 3 table