  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].
