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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM28820

Deep Learning-Based Image Colorization using AI

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM28820

Volume:

8 |

2 |

Feb 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM28820

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PA

Prof. A.D.Talole

SAP

Srushti Ashok Pawar

HSA

Harshada Santosh Ambekar

AMS

Aditi Mahesh Sonawane

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Abstract

This project aims to explore the application of deep learning techniques in image colorization using artificial intelligence (AI). Leveraging convolutional neural networks (CNNs) and generative adversarial networks (GANs), our objective is to develop a robust system capable of accurately and efficiently adding color to grayscale images. By training the model on a diverse dataset of grayscale images paired with their corresponding color versions, we aim to create a colorization tool that can replicate human-like color choices and produce visually appealing results. The proposed methodology involves preprocessing the dataset, designing and training the deep learning model, and evaluating its performance on test datasets. The anticipated outcome is a user-friendly image colorization tool that can be integrated into various applications, enhancing the visual experience and productivity of users.

KEYWORDS

Deep Learning Image Colorization Convolutional Neural Networks (CNNs) Generative Adversarial Networks (GANs) Artificial Intelligence (AI) Grayscale Images..

Cite This Article

Prof. A.D.Talole, Srushti Ashok Pawar, Harshada Santosh Ambekar, Aditi Mahesh Sonawane, "Deep Learning-Based Image Colorization using AI," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 2, Feb 2024. https://doi.org/10.55041/IJSREM28820

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Publication Date

12/31/45347

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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