Bayes-optimal inverse halftoning and statistical mechanics of the Q-Ising model

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 `Lenna', from the halftoned version. We find that the Bayes-optimal solution gives a fine restored grayscale image which is very close to the original.Comment: 13pages, 12figures, using elsart.cl

Analysis of Sample Correlations for Monte Carlo Rendering

Modern physically based rendering techniques critically depend on approximating integrals of high dimensional functions representing radiant light energy. Monte Carlo based integrators are the choice for complex scenes and effects. These integrators work by sampling the integrand at sample point locations. The distribution of these sample points determines convergence rates and noise in the final renderings. The characteristics of such distributions can be uniquely represented in terms of correlations of sampling point locations. Hence, it is essential to study these correlations to understand and adapt sample distributions for low error in integral approximation. In this work, we aim at providing a comprehensive and accessible overview of the techniques developed over the last decades to analyze such correlations, relate them to error in integrators, and understand when and how to use existing sampling algorithms for effective rendering workflows.publishe

Cognitive Information Processing

Contains goals, background, research activities on one research project and reports on three research projects.Center for Advanced Television StudiesAmerican Broadcasting CompanyAmpex CorporationColumbia Broadcasting SystemsHarris CorporationHome Box OfficePublic Broadcasting ServiceNational Broadcasting CompanyRCA CorporationTektronix3M CompanyProvidence Gravure Co. (Grant)International Business Machines, Inc

Fundamental Characteristics of Halftone Textures: Blue-Noise and Green-Noise

halftoning, dithering, blue noise, green noise In this paper, we review the spatial and spectral characteristics of blue and green-noise halftoning models. In the case of blue-noise, dispersed-dot dither patterns are constructed by isolating minority pixels as homogeneously as possible and by doing so, a pattern composed exclusively of high frequency spectral components is produced. Blue-noise halftoning is preferred for display devices that can accommodate isolated dots such as various video displays and some print technologies such as ink-jet. For print marking engines that cannot support isolated pixels dispersed-dot halftoning is inappropriate. For such cases, clustered-dot halftoning is used to avoid dot-gain instability. Green-noise halftones are clustered-dot blue noise patterns. Such patterns enjoy the blue-noise properties of homogeneity and lack of low frequency texture, but have clusters of minority pixels on blue-noise centers. Green noise is composed exclusively of mid-frequency spectral components. In addition to the basic spatial and spectral characteristics of the halftoning models, this paper also reviews some of the earlier work done to improve error diffusion as a noise generator. Also reviewed are processes to generate threshold arrays to achieve blue noise and green noise with the computationally efficient process of ordered dither