Wavelet-based restoration with tunable parameter

This paper demonstrates results of wavelet-based restoration for scenes with pixel-scale features, where noise amplification is controlled using a tunable parameter. The image acquisition model is chosen based on the so-called C/D/C system model that accounts for system blur, for the effects of aliasing, and for additive noise. By way of wavelet domain modeling, both the image acquisition kernel and the representations of scenes and images become discrete. Consequently, the image restoration problem is formulated as a discrete least squares problem in the wavelet domain. The treatment of noise is related to the singular values of the image acquisition kernel. Pixel-scale features can be restored exactly in the absence of noise, and results are similar in the presence of noise, except for some noise-amplification and truncation artifacts.(1-4) We devise an automated empirical procedure that provides a choice of the restoration parameter which conservatively avoids noise-ampification. This paper extends work in wavelet-based restoration,(1-4) and builds on research in C/D/C model-based restoration.(5-12).