Local and Global Graph Approaches to Image Colorization
Mamoru Sugawara, Kazunori Uruma, Seiichiro Hangai, Takayuki Hamamoto · IEEE Signal Processing Letters · 2020
Image colorization based on numerical modeling gives a highly accurate restoration result when colors are given to enough regions. A lot of numerical models focus on the relation between adjacent pixels; therefore, it is required to give the same color to various regions, and high spatial frequency regions are not colored properly. This letter proposes a colorization algorithm using graph signal processing. The key novelty of our algorithm is a new modeling method for a chrominance image using two different graphs. The first graph is a global graph, which connects the important pixels on an image. The second graph is a local graph, which connects the global graph and each pixel. Based on the hierarchical combination of these two graphs, color image is recovered. Numerical experiments show the effectiveness of the proposed algorithm by comparing with four existing methods.