WInHD: Wavelet-based Inverse Halftoning via Deconvolution


Journal PaperWe propose the <i>Wavelet-based Inverse Halftoning via Deconvolution</i> (WInHD) algorithm to perform inverse halftoning of error-diffused halftones. WInHD is motivated by our realization that inverse halftoning can be formulated as a deconvolution problem under Kite et al.'s linear approximation model for error diffusion halftoning. Under the linear model, the error-diffused halftone comprises the original gray-scale image blurred by a convolution operator and colored noise; the convolution operator and noise coloring are determined by the error diffusion technique. WInHD performs inverse halftoning by first inverting the model-specified convolution operator and then attenuating the residual noise using scalar wavelet-domain shrinkage. Since WInHD is model-based, it is easily adapted to different error diffusion halftoning techniques. Using simulations, we verify that WInHD is competitive with state-of-the-art inverse halftoning techniques in the mean-squared-error sense and that it also provides good visual performance. We also derive and analyze bounds on WInHD's mean-squared-error performance as the image resolution increases.National Science FoundationNational Science FoundationAir Force Office of Scientific Researc

Similar works

Full text


DSpace at Rice University

Full text is not available time updated on 6/11/2012

This paper was published in DSpace at Rice University.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.