Superresolution photoacoustic tomography using random speckle illumination and second order moments

TL;DR

This paper extends a superresolution microscopy technique based on speckle illumination and second order moments to photoacoustic tomography, introducing a fast linear algebra-based reconstruction algorithm and a new object representation.

Contribution

It adapts a superresolution method to photoacoustic imaging and proposes a computationally efficient reconstruction algorithm with a novel object representation.

Findings

The method achieves superresolution in photoacoustic tomography.

The proposed algorithm is faster than previous iterative methods.

A new Dirac delta-based object representation improves reconstruction.

Abstract

Idier et al. [IEEE Trans. Comput. Imaging 4(1), 2018] propose a method which achieves superresolution in the microscopy setting by leveraging random speckle illumination and knowledge about statistical second order moments for the illumination patterns and model noise. This is achieved without any assumptions on the sparsity of the imaged object. In this paper, we show that their technique can be extended to photoacoustic tomography. We propose a simple algorithm for doing the reconstruction which only requires a small number of linear algebra steps. It is therefore much faster than the iterative method used by Idier et al. We also propose a new representation of the imaged object based on Dirac delta expansion functions.