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WInHD: Wavelet-based Inverse Halftoning via Deconvolution

By Ramesh Neelamani, Robert David Nowak and Richard G. BaraniukRamesh Neelamani, Robert David Nowak and Richard G. Baraniuk


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

Topics: inverse halftoning, error diffusion, deconvolution, wavelets, wavelet-vaguelette, Image Processing and Pattern analysis, Wavelet based Signal/Image Processing, inverse halftoning, error diffusion, deconvolution, wavelets, wavelet-vaguelette
Year: 2002
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