Balinsky, Alexander ![]() |
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) |
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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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