Non-additive imprecise image super-resolution

This paper presents a new iterative image super-resolution technique. Its main feature is its ability to account for a lack of knowledge of the so called Point Spread Function (PSF) of the imager. This ability is based on representing the ill-known PSF by mean of a maxitive kernel, i.e. an imprecise PSF. It also uses the nice properties of a fuzzy partition of the image plane for defining projection and back-projection operators whose particularity is to output interval-valued images instead of precise valued ones. Those operators transfer the imprecise knowledge on the PSF to the output imprecision. We propose some experiments illustrating the robustness of our approach with respect to the registration errors and its performance compared to very competitive earlier approaches.