Digital halftoning
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Inverse halftoning and kernel estimation for error diffusion
IEEE Transactions on Image Processing
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On the basis of statistical mechanics of the Q-Ising model with ferromagnetic interactions under the random fields, we formulate the problem of inverse-halftoning for the error diffusion using the Floyd-Steinburg kernel. Then using the Monte Carlo simulation for a set of the snapshots of the Q-Ising model and a standard image, we estimate the performance of our method based on the mean square error and edge structures of the reconstructed image, such as the edge length and the gradient of the gray-level. We clarify that the optimal performance of the MPM estimate is achieved by suppressing the gradient of the gray-level on the edges of the halftone image and by removing a part of the halftone image if we set parameters appropriately.