Colorization by automatically generating the priori knowledge

A one-dimensional luminance scalar is replaced by a vector of a colorful multi-dimension for every pixel of a monochrome image, it is called colorization. Obviously, it is under-constrained. Thereby, the priori knowledge is considered to be given to the monochrome image. Colorization using optimization algorithm is an effective algorithm for the above problem. However, it cannot effectively do with some images well without repeating experiments for confirming the place of scribbles. In our paper, we propose a colorization algorithm which can automatically generate the priori knowledge. We show our idea as below. Firstly, the prior knowledge crystallizes into some points of the prior knowledge which is automatically extracted by downsampling and upsampling method. And then we classify some points of the prior knowledge and give the points of the prior knowledge the corresponding colors. Lastly, the color image can be obtained by the color points of the prior knowledge. We demonstrate that our proposal not only effectively generates the prior knowledge but also colorizes the monochrome image according to requirements of user with some experiments.