AI RESEARCH
NOIR: Neural Operator mapping for Implicit Representations
arXiv CS.CV
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ArXi:2603.13118v1 Announce Type: new This paper presents NOIR, a framework that reframes core medical imaging tasks as operator learning between continuous function spaces, challenging the prevailing paradigm of discrete grid-based deep learning. Instead of operating on fixed pixel or voxel grids, NOIR embeds discrete medical signals into shared Implicit Neural Representations and learns a Neural Operator that maps between their latent modulations, enabling resolution-independent function-to-function transformations.