Colorization by Patch-Based Local Low-Rank Matrix Completion

Colorization aims at recovering the original color of a monochrome image from only a few color pixels. A stateof-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 ADMM subproblem is solved efficiently by divide-and-conquer. Experiments on a number of benchmark images demonstrate that the proposed method outperforms existing approaches.

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Quanming Yao, James T. Kwok
• Keywords: AAAI 2015, Intelligent Agents