Signal Reconstruction from Samples at Unknown Locations with Application to 2D Unknown View Tomography

• URL: http://arxiv.org/abs/2304.06376v2
• Date: Wed, 18 Dec 2024 13:53:04 GMT
• Title: Signal Reconstruction from Samples at Unknown Locations with Application to 2D Unknown View Tomography
• Authors: Sheel Shah, Kaishva Shah, Karthik S. Gurumoorthy, Ajit Rajwade,
• Abstract summary: We prove that reconstruction methods are resilient to a certain proportion of errors in the specification of the sample location ordering. This is the first piece of work to perform such an analysis for 2D UVT and explicitly relate it to advances in sampling theory.
• Score: 2.284915385433677
• License:
• Abstract: It is well known that a band-limited signal can be reconstructed from its uniformly spaced samples if the sampling rate is sufficiently high. More recently, it has been proved that one can reconstruct a 1D band-limited signal even if the exact sample locations are unknown, but given a uniform distribution of the sample locations and their ordering in 1D. In this work, we extend the analytical error bounds in such scenarios for quasi-bandlimited (QBL) signals, and for the case of arbitrary but known sampling distributions. We also prove that such reconstruction methods are resilient to a certain proportion of errors in the specification of the sample location ordering. We then express the problem of tomographic reconstruction of 2D images from 1D Radon projections under unknown angles (2D UVT) with known angle distribution, as a special case for reconstruction of QBL signals from samples at unknown locations with known distribution. Building upon our theoretical background, we present asymptotic bounds for 2D QBL image reconstruction from 1D Radon projections in the unknown angles setting, and present an extensive set of simulations to verify these bounds in varied parameter regimes. To the best of our knowledge, this is the first piece of work to perform such an analysis for 2D UVT and explicitly relate it to advances in sampling theory, even though the associated reconstruction algorithms have been known for a long time.

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