Abstract-Colorization refers to an image processing task which recovers color in grayscale images when only small regions with color are given. We propose a couple of variational models using chromaticity color components to colorize black and white images. We first consider total variation minimizing (TV) colorization which is an extension from TV inpainting to color using chromaticity model. Second, we further modify our model to weighted harmonic maps for colorization. This model adds edge information from the brightness data, while it reconstructs smooth color values for each homogeneous region. We introduce penalized versions of the variational models, we analyze their convergence properties, and we present numerical results including extension to texture colorization.
A couple of variational models were proposed for image colorization in REF , which appropriately added edge information from the brightness data, while reconstructing smooth color values for each homogeneous region.
Variational Models for Image Colorization via Chromaticity and Brightness Decomposition
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Mathematics Medicine Computer Science