A Review and Analysis of The Existing Literature on Monochromatic Photography Colorization Using Deep Learning

Abstract

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 Networks. Received by the editors: TBA. 2010 Mathematics Subject Classification. 68T99. 1998 CR Categories and Descriptors. I.4.8 [Image Processing and Computer Vision]:Scene Annalysis – Color.

Keywords

• Photography Colorization, Deep Learning.