Image Colorizer using OpenCv and Convolutional Neural Networks

Registration ID: IJNRD_188216

Published ID: IJNRD2303086

DOI: Click Here to Get

Parthib Ranjan Ray , Akshay Narisetti , Dr.R.Renuka Devi

Image Colorization, Convolution neural networks, Feature extractor, Decoder

Given a grayscale photograph or video as input, this project attempts to create a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations. We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result. Colorizing images has a significant impact in different fields, such as photography of astronomical objects, the visuals of electronic microscopes, and CCTV surveillance systems. Using Deep Learning algorithms, we can build an automated system for analyzing color grayscale images.

Published Paper   E-Certificate

Parthib Ranjan Ray, Akshay Narisetti, & Dr.R.Renuka Devi (March-2023). Image Colorizer using OpenCv and Convolutional Neural Networks. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), a816-a819. https://ijnrd.org/papers/IJNRD2303086.pdf

Volume 8 Issue 3, March-2023

Pages : a816-a819

Paper Reg. ID: IJNRD_188216

Published Paper Id: IJNRD2303086

Downloads: 000121979

Research Area: Computer Engineering 

Country: Nagpur, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2303086.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303086

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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Call For Paper - Volume 10 | Issue 10 | October 2025

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