Wavelet-based inverse halftoning for error diffused halftones

This paper describes a technique for inverse halftoning based on the wavelet domain deconvolution that comprises Fourier-domain followed by wavelet-domain noise suppression, in order to benefit from the advantages of each of them. The proposed algorithm can be formulated as a linear deconvolution problem. In fact, we model such a gray-scale image to be the result of a convolution of the original image with a point spread function (PSF) and a colored noise. Our method performs inverse halftoning by first inverting the model specified convolution operator and then attenuating the residual noise using scalar wavelet-domain shrinkage. Using simulations, we verify that the proposed method is competitive with state-of-the-art inverse halftoning techniques in the mean-square-sense and that has also good visual performance. We illustrate the results with simulations on some examples.