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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15892
Title: Perceptual Conditional Generative Adversarial Networks for End-to-End Image Colourization
Authors: Halder S.S.
De K.
Pratim Roy, Partha
Jawahar C.V.
Mori G.
Schindler K.
Li H.
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract: 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 the proficiency of our methods through life-like colourization outcomes. © 2019, Springer Nature Switzerland AG.
Citation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), (2019), 269- 283
URI: https://doi.org/10.1007/978-3-030-20890-5_18
http://repository.iitr.ac.in/handle/123456789/15892
Issue Date: 2019
Publisher: Springer Verlag
Keywords: Colourization
Generative Adversarial Networks
Image Reconstruction
ISBN: 9.78303E+12
ISSN: 3029743
Author Scopus IDs: 57209322139
57204533373
56880478500
Author Affiliations: Halder, S.S., Indian Institute of Technology Roorkee, Roorkee, 247667, India
De, K., Indian Institute of Technology Roorkee, Roorkee, 247667, India
Roy, P.P., Indian Institute of Technology Roorkee, Roorkee, 247667, India
Corresponding Author: De, K.; Indian Institute of Technology RoorkeeIndia; email: kanjar.cspdf2017@iitr.ac.in
Appears in Collections:Conference Publications [CS]

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