Grayscale Image Colorization: A Literature Survey

Registration ID: IJNRD_191436

Published ID: IJNRD2311217

DOI: http://doi.one/10.1729/Journal.37730

Akshay Joshi , Rahul Patil , Rohit Joshi , Atharv Joshi , Ninad Hastak

Generative Adversarial Networks, Convolutional Neural Network, Grayscale, Automatic methods, Example-Based Methods, Image Quality Assessment, Scribble-Based Methods, User-Guided Methods, Colorization

The procedure of converting grayscale images to colour images in a way that is acceptable to the eyes is known as colorization. It is a very famous image to image translation problem and has been a topic of research ever since the advent of Computer Vision and Neural Networks. Grayscale image colorization has a number of uses cases some of which are colouring CCTV snapshots, restoring of old photos, transformation of interstellar objects captured through satellites, colorization of manga panels. Till now, techniques have been invented- some having straightforward algorithms yet consume a lot of time because of inevitable human intervention to complex but more automated and efficient methods. Today research is mostly focused on automatic image colorization techniques which has become a node that links deep learning with art. This paper provides an overview and evaluates the existing methods available to convert grayscale images to colour images. We classify the algorithms, explain their core principles and state their disadvantages and advantages. Evaluating the quality of a coloured image is difficult due to the complexity of the human visual system. The metrics used to assess colorization methods evaluate the difference between the predicted colour and the ground truth, but this difference may not always align with the perceived realism of the image. These state GAN based methods are most successful in colorizing a grayscale image. We foresee that our paper will drive the consideration of scientists to dig into GAN’s and develop efficient algorithms using them to colorize grayscale images. This review paper will highlight some of the various strategies that have been tested for the process of image colorization. Since ancient times, people have used many methods to colorize images. The current trend is toward totally automatic image colorization solutions. This paper provides an overview of various different approaches that have been attempted and implemented, as well as their benefits and drawbacks, as well as a comparison of them.

Published Paper   E-Certificate

Akshay Joshi, Rahul Patil, Rohit Joshi, Atharv Joshi, & Ninad Hastak (November-2023). Grayscale Image Colorization: A Literature Survey. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), c132-c136. http://doi.one/10.1729/Journal.37730

Volume 8 Issue 11, November-2023

Pages : c132-c136

Paper Reg. ID: IJNRD_191436

Published Paper Id: IJNRD2311217

Downloads: 000122005

Research Area: Engineering

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2311217.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2311217

Crossref DOI: http://doi.one/10.1729/Journal.37730

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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