  We present MR-Net, a general architecture for multiresolution neural
networks, and a framework for imaging applications based on this architecture.
Our coordinate-based networks are continuous both in space and in scale as they
are composed of multiple stages that progressively add finer details. Besides
that, they are a compact and efficient representation. We show examples of
multiresolution image representation and applications to texturemagnification,
minification, and antialiasing. This document is the extended version of the
paper [PNS+22]. It includes additional material that would not fit the page
limitations of the conference track for publication.
