Open AccessA Review and Analysis of The Existing Literature on Monochromatic Photography Colorization Using Deep Learning
Author(s) -
Ana Maria Adascalitei
Publication year - 2021
Publication title -
studia universitatis babeş-bolyai. informatica
Language(s) - English
Resource type - Journals
eISSN - 2065-9601
pISSN - 1224-869X
DOI - 10.24193/subbi.2021.2.03
Subject(s) - photography , computer science , monochrome , generative grammar , deep learning , key (lock) , artificial intelligence , software , computational photography , data science , reliability (semiconductor) , multimedia , image processing , image (mathematics) , visual arts , art , programming language , computer security , power (physics) , physics , quantum mechanics
It is universally known that, through the process of colorization, one aims at converting a monochrome image into one of color, usually because it was taken by the limited technology of previous decades. Our work introduces the problem, summarizes the general deep learning solutions, and discusses the experimental results obtained from open-source repositories. Although the surveyed methods can be applied to other fields, solely the content of photography is being considered. Our contribution stands in the analysis of colorization in photography by examining used datasets and methodologies for evaluation, data processing activities, and the infrastructure demanded by these systems. We curated some of the most promising papers, published between 2016 and 2021, and centered our observations around software reliability, and key advancements in solutions employing Generative Adversarial Networks and Neural Networ