Colorization of Natural Images via L1 Optimization
Abstract
Natural images in the colour space YUV have been observed to have a nonGaussian, 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 nonconvex 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, overperforms the famous colorization algorithm by Levin et al [3].
 Publication:

arXiv eprints
 Pub Date:
 May 2009
 arXiv:
 arXiv:0905.2924
 Bibcode:
 2009arXiv0905.2924M
 Keywords:

 Computer Science  Computer Vision and Pattern Recognition
 EPrint:
 5 pages, 3 figures