An efficient PCA-based color transfer method

Color information of natural images can be considered as a highly correlated vector space. Many different color spaces have been proposed in the literature with different motivations toward modeling and analysis of this stochastic field. Recently, color transfer among different images has been under investigation. Color transferring consists of two major categories: colorizing grayscale images and recoloring colored images. The literature contains a few color transfer methods that rely on some standard color spaces. In this paper, taking advantages of the principal component analysis (PCA), we propose a unifying framework for both mentioned problems. The experimental results show the efficiency of the proposed method. The performance comparison of the proposed method is also given.