Saved in:
Bibliographic Details
Main Authors: Deng, Zhanpeng, Li, Jiao, Xian, Jun
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2511.02261
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866914134065414144
author Deng, Zhanpeng
Li, Jiao
Xian, Jun
author_facet Deng, Zhanpeng
Li, Jiao
Xian, Jun
contents In this paper, we deal with the problem of reconstruction from Radon random samples in local shift-invariant signal space. Different from sampling after Radon transform, we consider sampling before Radon transform, where the sample set is randomly selected from a square domain with a general probability distribution. First, we prove that the sampling set is stable with high probability under a sufficiently large sample size. Second, we address the problem of signal reconstruction in two-dimensional computed tomography. We demonstrate that the sample values used for this reconstruction process can be determined completely from its Radon transform data. Consequently, we develop an explicit formula to reconstruct the signal using Radon random samples.
format Preprint
id arxiv_https___arxiv_org_abs_2511_02261
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Radon random sampling and reconstruction in local shift-invariant signal space
Deng, Zhanpeng
Li, Jiao
Xian, Jun
Optimization and Control
Information Theory
In this paper, we deal with the problem of reconstruction from Radon random samples in local shift-invariant signal space. Different from sampling after Radon transform, we consider sampling before Radon transform, where the sample set is randomly selected from a square domain with a general probability distribution. First, we prove that the sampling set is stable with high probability under a sufficiently large sample size. Second, we address the problem of signal reconstruction in two-dimensional computed tomography. We demonstrate that the sample values used for this reconstruction process can be determined completely from its Radon transform data. Consequently, we develop an explicit formula to reconstruct the signal using Radon random samples.
title Radon random sampling and reconstruction in local shift-invariant signal space
topic Optimization and Control
Information Theory
url https://arxiv.org/abs/2511.02261