Inverse halftoning by decision tree learning

Inverse halftoning is the process to retrieve a (gray) continuous-tone image from a halftone. Recently, machine-learning-based inverse halftoning techniques have been proposed. Decision-tree learning has been applied with success to various machine-learning applications for quite some time. In this paper, we propose to use decision-tree learning to solve the inverse halftoning problem. This allows us to reuse a number of algorithms already developed. Especially, the maximization of entropy gain is a powerful idea that makes the learning algorithm to automatically select the ideal window as the decision-tree is constructed. The new technique has generated gray images with PSNR numbers, which are several dB above those previously reported in the literature. Moreover, it possesses very fast implementation, lending itself useful for real time applications.