<title>Wavelet-based restoration with tunable parameter</title>

Abstract

This paper demonstrates result 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 an image become discrete. Consequently, the image acquisition kernel and the representations of scenes and images become discrete. Consequently, the image restoration problem is formed as a discrete least squares problem in the wavelet domain. The treatment of noise is real to the singular values of the image acquisition kernel. Pixel-scale features can be restored exactly in the absence of noise, and result are similar in the presence of noise, except for some noise- amplification and truncation artifacts. We devise an automated empirical procedure that provides a choice of the restoration parameters which conservatively avoids noise- amplification. This paper extends work in wavelet-based restoration, and builds on research in C/D/C model-based restoration.