Color Transfer Using Adaptive Second-Order Total Generalized Variation Regularizer

Abstract

Color transfer is to generate synthetic images by changing the color of target images with new colors obtained from given source images, while the geometrical structure of the synthetic images remains the same. Classical color transfer models use a total variation (TV) regularizer to preserve the details and suppress the noise of the synthetic images. These models can sometimes cause staircase effect and geometrical structure details over-smoothed. To overcome these problems, we propose a new color transfer model in which an adaptive second-order total generalized variation (TGV) regularizer is designed. Here, the adaptive second-order TGV regularizer is a weighted second-order TGV regularizer. The weight is computed by an adaptive edge indicator function. In addition, an efficient algorithm is developed to program our new model. The algorithm is based on a weighted primal-dual method. Experimental results and comparisons demonstrate that our new color transfer model can generate better results than classical TV regularizer-based models in the aspects of the inhibition of staircase effect and the preservation of image details.

Keywords

• Color transfer
• staircase effect
• total variation (TV)
• total generalized variation (TGV)
• primal-dual algorithm