TY - JOUR AU - Yao, Quanming AU - James, T. Kwok PY - 2015/02/18 Y2 - 2024/03/29 TI - Colorization by Patch-Based Local Low-Rank Matrix Completion JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 29 IS - 1 SE - Main Track: Machine Learning Applications DO - 10.1609/aaai.v29i1.9479 UR - https://ojs.aaai.org/index.php/AAAI/article/view/9479 SP - AB - <p> Colorization aims at recovering the original color of a monochrome image from only a few color pixels. A state-of-the-art approach is based on matrix completion, which assumes that the target color image is low-rank. However, this low-rank assumption is often invalid on natural images. In this paper, we propose a patch-based approach that divides the image into patches and then imposes a low-rank structure only on groups of similar patches. Each local matrix completion problem is solved by an accelerated version of alternating direction method of multipliers (ADMM), and each AD-MM subproblem is solved efficiently by divide-and-conquer. Experiments on a number of benchmark images demonstrate that the proposed method outperforms existing approaches. </p> ER -