WInHD: Wavelet-based Inverse Halftoning via Deconvolution

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Title: WInHD: Wavelet-based Inverse Halftoning via Deconvolution
Author: Neelamani, Ramesh; Nowak, Robert David; Baraniuk, Richard G.
Type: Journal Paper
Keywords: inverse halftoning; error diffusion; deconvolution; wavelets; wavelet-vaguelette
Citation: R. Neelamani, R. D. Nowak and R. G. Baraniuk, "WInHD: Wavelet-based Inverse Halftoning via Deconvolution," IEEE Transactions on Image Processing, 2002.
Abstract: We propose the Wavelet-based Inverse Halftoning via Deconvolution (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.
Date Published: 2002-10-20

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