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arXiv · 0905.2924

Colorization of Natural Images via L1 Optimization

Nassir Mohammad·Alexander Balinsky
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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 G(U)(r) = U(r) - sum_{s \in N(r)} w{(Y)_{rs}} U(s), is applied to the chromacity channel U (and equivalently to V), where w is a weighting function constructed from the intensity component Y [1]. 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 [2]. It is observed that L1 optimization, in many cases, over-performs the famous colorization algorithm by Levin et al [3].

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Nassir Mohammad, Alexander Balinsky. 2009-05-18. Colorization of Natural Images via L1 Optimization. https://arxiv.org/abs/0905.2924

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