Recent Approaches for Image Colorization

Fabien Pierre () and Jean-François Aujol ()
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Fabien Pierre: Université de Lorraine, INRIA projet Tangram, LORIA, UMR CNRS 7503
Jean-François Aujol: Univ. Bordeaux, Bordeaux INP, CNRS, IMB, UMR 5251

Chapter 15 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 585-622 from Springer

Abstract: Abstract In the last years, image and video colorization has been considered from many points of view. The technique consists of the addition of a color component to a grayscale image. This operation needs additional priors which can be given by manual intervention of the user from an example image or be extracted from a large dataset of color images. A very large variety of approaches has been used to solve this problem, like PDE models, non-local methods, variational frameworks, learning approaches, etc. In this chapter, we aim at providing a general overview of state-of-the-art approaches with a focus on few representative methods. Moreover, some recent techniques from the different types of priors (manual, exemplar-based, dataset-based) are explained and compared. The organization of the chapter aims at describing the evolution of the techniques in relation to each other. A focus on some efficient strategies is proposed for each kind of methodology.

Keywords: Image colorization; Variational approaches; Deep learning; Patch-based methods (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-98661-2_55

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DOI: 10.1007/978-3-030-98661-2_55

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