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arXiv · 2208.11813

Multiresolution Neural Networks for Imaging

Hallison Paz·Tiago Novello·Vinicius Silva·Luiz Schirmer·Guilherme Schardong·Fabio Chagas·Helio Lopes·Luiz Velho
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Abstract

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.

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Hallison Paz, Tiago Novello, Vinicius Silva, Luiz Schirmer, Guilherme Schardong, Fabio Chagas, Helio Lopes, Luiz Velho. 2022-09-10. Multiresolution Neural Networks for Imaging. https://arxiv.org/abs/2208.11813

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