The aim of the project is to improve and automate the data processing pipeline of underwater imaging systems currently under development as a longstanding collaboration between HWU and UoE. Home rate fees and a stipend are available for this project.UK Nationals only.ObjectivesImproving the quality of images for underwater single-photon active/passive dataSensor fusion with single-photon Lidar and sonar dataAdaptive sensingDescriptionThe development of single-photon detector arrays has opened exciting opportunities for passive and active imaging in extreme conditions (high-speed, low-illumination regimes), and in particular for imaging in scattering underwater environments. In this project we will first investigate multiband (multispectral/polarimetric) imaging in the high-background, low-photon regime, where traditional image restoration methods fail due to the non-Gaussian noise statistics. In the context of 3D imaging, single-photon Lidar offers unprecedented sensitivity and range resolution compared to alternative technologies but needs to be guided (e.g., by restricting the depth of field) to reduce the impact of noise. Here, we propose to use sonar data to coarsely identify underwater objects of interest and adaptively optimised the single-photon Lidar system. From a computational viewpoint, this project will investigate physics-based models as well as data-driven models for fusion and computational complexity analysis will be crucial to ensure rapid sensor feedback/control. Further Informationhttps://opg.optica.org/oe/fulltext.cfm?uri=oe-31-10-16690https://www.imagesensors.org/Past%20Workshops/2024%20ISSW/Papers/R05.3.pdf Research themeSensor Signal ProcessingIndustry PartnerThalesPrincipal supervisorDr Aurora MaccaroneHeriot-Watt University, School of EngineeringA.Maccarone@hw.ac.ukAssistant supervisorsProf Yoann AltmannHeriot-Watt University, School of Engineering and Physical SciencesY.Altmann@hw.ac.uk Dr Istvan GyongyUniversity of Edinburgh, School of Engineeringigyongy2@ed.ac.uk This article was published on 2025-10-31