2009/05/18 by Nassir Mohammad, Mohammad, Nassir, Alexander Balinsky +1
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Image Enhancement Techniques #Image and Signal Denoising Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.0905.2924
5 pages, 3 figures
arxiv created 2009/05/18 · openalex publication_date 2009/05/18 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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) - sums ∈ 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].