Implicit neural signal processing refers to the methodology and computational techniques for directly filtering, transforming, and manipulating continuous signals encoded within implicit neural representations without first decoding them into discrete formats. Unlike traditional workflows that require sampling neural coordinate networks into explicit pixel grids, voxel arrays, or meshes to apply transformations, implicit neural signal processing operates directly on the underlying neural representations, network parameters, or analytical computational graphs. By leveraging mathematical tools such as continuous differential operators, analytical gradients, and functional compositions, this approach enables operations such as spatial convolution, filtering, denoising, and geometric modification to be executed natively while preserving the resolution-independent and continuous properties of the implicit representation.
