Coded aperture imaging using non-linear Lucy-Richardson algorithm

doi:10.1016/j.optlastec.2024.112300

Coded aperture imaging using non-linear Lucy-Richardson algorithm

2025-05-01

1

PRE

AI

A

Agnes Pristy Ignatius Xavier *

T

Tauno Kahro

S

Shivasubramanian Gopinath

Aravind Simon John Francis Rajeswary

DOI：10.1016/j.optlastec.2024.112300

Original

Original request for help

Save

Abstract

En 中文

Imaging involves the process of recording and reproducing images as close to reality as possible, encompassing both direct and indirect approaches. In direct imaging, the object is directly recorded. Coded aperture imaging (CAI) is an example of indirect imaging, that utilizes optical recording and computational reconstruction to obtain information about an object. Computational reconstruction can be achieved using different linear, nonlinear, iterative, and deep learning algorithms. In this study, we proposed and demonstrated two computational reconstruction algorithms based on the non-linear Lucy-Richardson algorithm (NL-LRA), one for limited support images and another for full-view images based on entropy reduction. The efficacy of these algorithms has been validated through simulations and optical experiments carried out in visible and infrared (IR) light with different coded phase masks. The methods were also tested on a commercial IR microscope with internal GlobarTM and synchrotron sources. The results obtained from the two algorithms were compared with those from their parent methods, and a notable improvement in both entropy and the convergence rate was observed. We believe the developed algorithms will drastically improve image reconstruction in incoherent imaging applications.

Keywords:

Coded aperture imaging

Infrared imaging

Computational imaging

Non-linear Lucy-Richardson algorithm

Diffractive optics

Photolithography

Coded aperture imaging using non-linear Lucy-Richardson algorithm

Abstract

Journal

Organization