Natural images in the colour space Y U V have been observed to have a non-Gaussian, heavy tailed distribution (called 'sparse') when the filter 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 L 1 optimization which often gives the same results for sparse signals [4]. It is observed that L 1 optimization, in many cases, over-performs the colorization algorithm of Levin et al. [5] .
Balinsky and Mohammad REF put forward a Bayesian analysis of the colorization problem that is convexified by using 1 optimization.
Colorization of natural images via L1 optimization
2009 Workshop on Applications of Computer Vision (WACV)
2009 Workshop on Applications of Computer Vision (WACV)
Computer Science Mathematics