Markov-Chain Monte Carlo Simulation of Inverse-Halftoning for Error Diffusion based on Statistical Mechanics of the Q-Ising Model

摘要

On the basis of statistical mechanics of the Q-Ising model we formulate the problem of inverse-halftoning for the halftone image which is obtained by the error diffusion method using the Floyd-Steinburg and two weight kernels. Then using the Markov-Chain Monte Carlo simulation both for a set of the snapshots of the Q-Ising model and a gray-level standard image, we estimate the performance of our method based on the mean square error and the edge structures observed both in the halftone image and reconstructed images, such as the edge length and the gradient of the gray-level. We clarify that our method reconstructs the gray-level image from the halftone image by suppressing the gradient of the gray-level on the edges embedded in the halftone image and by removing a part of the edges if we appropriately set parameters of our model.