Image Colour Prediction using Deep learning
K. Vishnu Prakash1, S. Siva Prakash2, H. Vishnu Harichandran3, S Petchiappan4, I Muthu Selvi5
1K. Vishnu Prakash*, UG Student, Department of CSE, National Engineering, Kovilpatti, India.
2S. Siva Prakash, UG Student, Department of CSE, National Engineering, Kovilpatti, India.
3H.Vishnu Harichandran, UG Student, Department of CSE, National Engineering, Kovilpatti, India.
4S Petchiappan, UG Student, Department of CSE, National Engineering, Kovilpatti, India.
5Mrs. I Muthu selvi, Assistant Professor, Department of CSE, National Engineering, Kovilpatti, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 2037-2040 | Volume-8 Issue-6, March 2020. | Retrieval Number: E5935018520/2020©BEIESP | DOI: 10.35940/ijrte.E5935.038620
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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: Any image we perceive through a screen is made of three separate channels, R, G, and B. With the help of these three channels; an image comes to colour. Any pictures taken during the old times were in grayscale format. To convert any given grayscale image into colour, we need the help of a photoshop professional, which might take hours of the workforce. In a revolution to this, we propose an utterly programmed methodology that produces lively and practical colourizations. Generative adversarial networks are an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input file to get an output. In our case, a grayscale image can be converted to colour with the help of GANs.
Keywords: Spam, Channels, Colorizations, GAN, Grayscale, Learning, Photoshop, Unsupervised..
Scope of the Article: Deep learning.