Colorization of natural images via L1 optimization

Balinsky, Alexander ORCID: https://orcid.org/0000-0002-8151-4462 and Mohammad, Nassir 2009. Colorization of natural images via L1 optimization. Presented at: IEEE Workshop on Applications of Computer Vision, Snowbird, UT, USA, 7-8 December 2009. IEEE 2009 Workshop on Applications of Computer Vision WACV 2009, Dec. 7-8 2009, Snowbird Utah. IEEE Computer Society, pp. 1-6.

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Abstract

Natural images in the colour space YUV have been observed to have a non-Gaussian, heavy tailed distribution (called `sparse') when the filter ¿(U)(r) = U(r) - s¿N(r)¿ w(Y)rsU(s), is applied to the chromacity channel U (and equivalently to V), where w is a weighting function constructed from the intensity component Y. In this paper we develop Bayesian analysis of the colorization problem using the filter response as a regularization term to arrive at a non-convex optimization problem. This problem is convexified using L1 optimization which often gives the same results for sparse signals. It is observed that L1 optimization, in many cases, over-performs the colorization algorithm of Levin et al..

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	Bayesian methods; Color; Cost function; Filters; Histograms; Image processing; Laboratories; Layout; Mathematics; Moon.
Publisher:	IEEE Computer Society
ISBN:	9781424454976
Last Modified:	19 Oct 2022 10:28
URI:	https://orca.cardiff.ac.uk/id/eprint/24444

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