Eleven Primitives and Three Gates: The Universal Structure of Computational Imaging
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Abstract (arXiv)
Computational imaging systems -- from coded-aperture cameras to cryo-electron microscopes -- span five carrier families yet share a hidden structural simplicity. We prove that every imaging forward model decomposes into a directed acyclic graph over exactly 11 physically typed primitives (Finite Primitive Basis Theorem) -- a sufficient and minimal basis that provides a compositional language for designing any imaging modality. We further prove that every reconstruction failure has exactly three independent root causes: information deficiency, carrier noise, and operator mismatch (Triad Decomposition). The three gates map to the system lifecycle: Gates 1 and 2 guide design (sampling geometry, carrier selection); Gate 3 governs deployment-stage calibration and drift correction. Validation across 12 modalities and all five carrier families confirms both results, with +0.8 to +13.9 dB recovery on deployed instruments. Together, the 11 primitives and 3 gates establish the first universal grammar for designing, diagnosing, and correcting computational imaging systems.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 53 references that are in the map. First posted on arXiv 13 Mar 2026.
Builds on
11 of 53 references.
References · 11
- 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 411
- 2019On instabilities of deep learning in image reconstruction - Does AI come at a cost?cited by 211
- 2021Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstructioncited by 110
- 2022HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imagingcited by 66
- 2021Deep Gaussian Scale Mixture Prior for Spectral Compressive Imagingcited by 58
- 2013Coded aperture compressive temporal imagingcited by 57
- 2017DiffuserCam: Lensless Single-exposure 3D Imagingcited by 51
- 2021Plug-and-Play Algorithms for Video Snapshot Compressive Imagingcited by 42
- 2023EfficientSCI: Densely Connected Network with Space-time Factorization for Large-scale Video Snapshot Compressive Imagingcited by 18
- 2017Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learningcited by 7
- 2026The Finite Primitive Basis Theorem for Computational Imaging: Formal Foundations of the OperatorGraph Representationcited by 1
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Citing papers per year: 2026: 1.
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