Inverse color to black-and-white halftone conversion via dictionary learning and color mapping

This paper challenges the problem of estimating the original red–green–blue (RGB) image from a black-and-white (B&W) halftone image with homogeneously distributed dot patterns. To achieve this goal, training RGB images are converted into color-embedded gray images using the conventional reversible color to gray conversion method, and then converted into halftone images using error diffusion in order to produce the corresponding B&W halftone images. The proposed method is composed of two processing steps: (1) restoring the color-embedded gray image from an input B&W halftone image using a sparse linear representation between the image patch pairs obtained from the images and (2) restoring the original colors from the color-embedded gray image using the reversible color to gray conversion and linear color mapping methods. The proposed method successfully demonstrates the recovery of colors similar to the originals. The experimental results indicate that the proposed method outperforms the conventional methods. It is suggested that our method is not only successfully applied for the color recovery of the B&W halftone image, but that it can also be extended to various applications including color restoration of printed image, hardcopy data hiding, and halftone color compression.