Work

Bayes-optimal solution to inverse halftoning based on statistical mechanics of the Q-Ising model

Year: 2009

Type: article

Abstract: On the basis of statistical mechanics of the Q-Ising model, we formulate the Bayesian inference to the problem of inverse halftoning, which is the inverse process of representing gray-scales in images by means of black and white dots. Using Monte Carlo simulations, we investigate statistical properties of the inverse process, especially, we reveal the condition of the Bayes-optimal solution for which the mean-square error takes its minimum. The numerical result is qualitatively confirmed by analysis of the infinite-range model. As demonstrations of our approach, we apply the method to retrieve a grayscale image, such as standard image Lena, from the halftoned version. We find that the Bayes-optimal solution gives a fine restored grayscale image which is very close to the original. In addition, based on statistical mechanics of the Q-Ising model, we are sucessful in constructing a practically useful method of inverse halftoning using the Bethe approximation.

Source: Open Physics

DOI: 10.2478/s11534-009-0066-0

Authors: Yohei Saika, Jun-ichi Inoue, Hiroyuki Tanaka, Masato Okada

Institutions: National College of Technology, Wakayama College, Hokkaido University, The University of Tokyo

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $1,356

Funder: Ministry of Education, Culture, Sports, Science and Technology