From 2D Sketches to Photo-Realistic Images using Generative Adversarial Networks
Ekta M. Upadhyay
Dr. Ekta M. Upadhyay*, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
Manuscript received on October 13, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 2414-2417 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4360119119/2019©BEIESP | DOI: 10.35940/ijitee.A4360.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With increasing technological advancements, there is a need for automation in this ever-evolving world. This may result in improved efficiency, faster work and enhanced capabilities. Sketch-to-image translation is an image processing application that can be used as a helping hand in a variety of fields. One of these is the utilization of Generative Adversarial Networks to guide edges to photographs, with the assistance of image generators and discriminators who work connected to produce realistic images. We have also incorporated Histogram of Oriented Gradients (HOG) as a feature/image descriptor. The HOG technique counts the gradient orientations to differentiate the target and the background. Support Vector Machine (SVM) is the classifier used for classification. This HOG and SVM model can be improved, altered and executed as multi-program software.
Keywords: Sketch, Photo Realistic Image, Generative Adversarial Network, Histogram of Oriented Gradients
Scope of the Article: Sensor Networks, Actuators for Internet of Things