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Influence of Color Spaces for Deep Learning Image Colorization

In: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

Author

Listed:
  • Aurélie Bugeau

    (Univ. Bordeaux, LaBRI, CNRS, UMR5800
    Institut universitaire de France (IUF))

  • Rémi Giraud

    (Bordeaux INP, Univ. Bordeaux, CNRS, IMS UMR5251)

  • Lara Raad

    (Univ Gustave Eiffel, LIGM, CNRS)

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.

Suggested Citation

  • Aurélie Bugeau & Rémi Giraud & Lara Raad, 2023. "Influence of Color Spaces for Deep Learning Image Colorization," Springer Books, in: Ke Chen & Carola-Bibiane Schönlieb & Xue-Cheng Tai & Laurent Younes (ed.), Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, chapter 22, pages 847-878, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-98661-2_125
    DOI: 10.1007/978-3-030-98661-2_125
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