Spectral analysis of the truncated Hilbert transform with overlap

TL;DR

This paper analyzes the spectral properties of a restricted Hilbert transform relevant to limited-data tomography, revealing its ill-posedness and spectral structure through a Sturm-Liouville problem.

Contribution

It establishes the spectral discreteness of the truncated Hilbert transform and links it to a Sturm-Liouville problem, providing a detailed spectral analysis.

Findings

The operator's spectrum is discrete and accumulates at 0 and 1.

The inverse problem for the truncated Hilbert transform is ill-posed.

The singular values of the operator are explicitly characterized.

Abstract

We study a restriction of the Hilbert transform as an operator $H_T$ from $L^2(a_2,a_4)$ to $L^2(a_1,a_3)$ for real numbers $a_1 < a_2 < a_3 < a_4$. The operator $H_T$ arises in tomographic reconstruction from limited data, more precisely in the method of differentiated back-projection (DBP). There, the reconstruction requires recovering a family of one-dimensional functions $f$ supported on compact intervals $[a_2,a_4]$ from its Hilbert transform measured on intervals $[a_1,a_3]$ that might only overlap, but not cover $[a_2,a_4]$. We show that the inversion of $H_T$ is ill-posed, which is why we investigate the spectral properties of $H_T$. We relate the operator $H_T$ to a self-adjoint two-interval Sturm-Liouville problem, for which we prove that the spectrum is discrete. The Sturm-Liouville operator is found to commute with $H_T$, which then implies that the spectrum of $H_T^* H_T$ is discrete. Furthermore, we express the singular value decomposition of $H_T$ in terms of the solutions to the Sturm-Liouville problem. The singular values of $H_T$ accumulate at both $0$ and $1$, implying that $H_T$ is not a compact operator. We conclude by illustrating the properties obtained for $H_T$ numerically.