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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Grayscale Image Colorization: A Literature Survey |
| Authors Name: | Akshay Joshi , Rahul Patil , Rohit Joshi , Atharv Joshi , Ninad Hastak |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_191436
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| Published Paper Id: | IJNRD2311217 |
| Published In: | Volume 8 Issue 11, November-2023 |
| DOI: | http://doi.one/10.1729/Journal.37730 |
| Abstract: | 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. |
| Keywords: | Generative Adversarial Networks, Convolutional Neural Network, Grayscale, Automatic methods, Example-Based Methods, Image Quality Assessment, Scribble-Based Methods, User-Guided Methods, Colorization |
| Cite Article: | "Grayscale Image Colorization: A Literature Survey", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 11, page no.c132-c136, November-2023, Available :http://www.ijnrd.org/papers/IJNRD2311217.pdf |
| Downloads: | 000118749 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2311217 Registration ID: 191436 Published In: Volume 8 Issue 11, November-2023 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.37730 Page No: c132-c136 Country: Pune, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2311217 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2311217 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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