Performance of generalized statistical smoothing to inverse halftoning due to human vision system

We constructed a method of inverse halftoning using generalized statistical smoothing (GSS) for a halftone image obtained by converting a grayscale image with the use of error diffusion method via the Floyd-Steinberg kernel. This method was constructed by introducing edge enhancement and generalized...

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Bibliographic Details
Published inThe 4th International Conference on Interaction Sciences pp. 27 - 30
Main Authors Saika, Y., Okamoto, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2011
Subjects
Humans
Image edge detection
Image reconstruction
Image restoration
Machine vision
Mean square error methods
Smoothing methods
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Summary:We constructed a method of inverse halftoning using generalized statistical smoothing (GSS) for a halftone image obtained by converting a grayscale image with the use of error diffusion method via the Floyd-Steinberg kernel. This method was constructed by introducing edge enhancement and generalized parameter scheduling into a method of statistical smoothing (SS) proposed by Wong. In order to clarify the performance of the GSS, we evaluated both the mean square error and the mean square error modulated by the MTF function representing the sensitivity of human vision system. Numerical simulations for 256-grayscale standard images showed that optimal performance is achieved by introducing the appropriate models of edge enhancement and generalized parameter scheduling. Also, we found that the optimal performance is superior to those of the SS and the conventional Gaussian filter.
ISBN:9781457704802
1457704803