Influence of Color Spaces for Deep Learning Image Colorization

Aurélie Bugeau (), Rémi Giraud () and Lara Raad ()
Additional contact information
Aurélie Bugeau: Univ. Bordeaux, LaBRI, CNRS, UMR5800
Rémi Giraud: Bordeaux INP, Univ. Bordeaux, CNRS, IMS UMR5251
Lara Raad: Univ Gustave Eiffel, LIGM, CNRS

Chapter 22 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 847-878 from Springer

Abstract: Abstract Colorization is a process that converts a grayscale image into a colored one that looks as natural as possible. Over the years this task has received a lot of attention. Existing colorization methods rely on different color spaces: RGB, YUV, Lab, etc. In this chapter, we aim to study their influence on the results obtained by training a deep neural network, to answer the following question: “Is it crucial to correctly choose the right color space in deep learning-based colorization?” First, we briefly summarize the literature and, in particular, deep learning-based methods. We then compare the results obtained with the same deep neural network architecture with RGB, YUV, and Lab color spaces. Qualitative and quantitative analysis do not conclude similarly on which color space is better. We then show the importance of carefully designing the architecture and evaluation protocols depending on the types of images that are being processed and their specificities: strong/small contours, few/many objects, recent/archive images.

Keywords: Image colorization; Deep learning; Color spaces (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-98661-2_125

Ordering information: This item can be ordered from
http://www.springer.com/9783030986612

DOI: 10.1007/978-3-030-98661-2_125

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().