Colorization of Natural Images via L1 Optimization
| dc.creator | Mohammad, Nassir | |
| dc.creator | Balinsky, Alexander | |
| dc.date | 2009-05-18 | |
| dc.date.accessioned | 2026-07-07T13:16:04Z | |
| dc.date.available | 2026-07-07T13:16:04Z | |
| dc.description | 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]. | |
| dc.description | 5 pages, 3 figures | |
| dc.identifier | https://arxiv.org/abs/0905.2924 | |
| dc.identifier | http://arxiv.org/abs/0905.2924 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/230671 | |
| dc.subject | Computer Vision and Pattern Recognition | |
| dc.title | Colorization of Natural Images via L1 Optimization | |
| dc.type | text |