The Finite Primitive Basis Theorem for Computational Imaging: Formal Foundations of the OperatorGraph Representation

• URL: http://arxiv.org/abs/2602.20550v1
• Date: Tue, 24 Feb 2026 05:07:06 GMT
• Title: The Finite Primitive Basis Theorem for Computational Imaging: Formal Foundations of the OperatorGraph Representation
• Abstract summary: We prove that every forward model in a broad, precisely defined operator class Cimg admits an epsilon-approximate representation as a typed directed acyclic graph (DAG)<n>We provide an algorithm that, given any H in Cimg, produces a DAG G with relative operator error at most epsilon and graph complexity within prescribed bounds.
• Score: 1.7259824817932294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational imaging forward models, from coded aperture spectral cameras to MRI scanners, are traditionally implemented as monolithic, modality-specific codes. We prove that every forward model in a broad, precisely defined operator class Cimg (encompassing clinical, scientific, and industrial imaging modalities, both linear and nonlinear) admits an epsilon-approximate representation as a typed directed acyclic graph (DAG) whose nodes are drawn from a library of exactly 11 canonical primitives: Propagate, Modulate, Project, Encode, Convolve, Accumulate, Detect, Sample, Disperse, Scatter, and Transform. We call this the Finite Primitive Basis Theorem. The proof is constructive: we provide an algorithm that, given any H in Cimg, produces a DAG G with relative operator error at most epsilon and graph complexity within prescribed bounds. We further prove that the library is minimal: removing any single primitive causes at least one modality to lose its epsilon-approximate representation. A systematic analysis of nonlinearities in imaging physics shows they fall into two structural categories: pointwise scalar functions (handled by Transform) and self-consistent iterations (unrolled into existing linear primitives). Empirical validation on 31 linear modalities confirms eimg below 0.01 with at most 5 nodes and depth 5, and we provide constructive DAG decompositions for 9 additional nonlinear modalities. These results establish mathematical foundations for the Physics World Model (PWM) framework.

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