Overlapping speckle correlation algorithm for high-resolution imaging and tracking of objects in unknown scattering media

• Published in: Nature communications Vol. 14; no. 1; p. 7742
• Main Authors: Shi, Yaoyao, Sheng, Wei, Fu, Yangyang, Liu, Youwen
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.11.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/624/1107; 639/624/1107/510; Algorithms; High resolution; Humanities and Social Sciences; Image resolution; Light emitting diodes; Media; multidisciplinary; Object motion; Scattering; Science; Science (multidisciplinary); Thin films; Tracking; White light
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-43674-5

Optical imaging in scattering media is important to many fields but remains challenging. Recent methods have focused on imaging through thin scattering layers or thicker scattering media with prior knowledge of the sample, but this still limits practical applications. Here, we report an imaging method named ‘speckle kinetography’ that enables high-resolution imaging in unknown scattering media with thicknesses up to about 6 transport mean free paths. Speckle kinetography non-invasively records a series of incoherent speckle images accompanied by object motion and the inherently retained object information is extracted through an overlapping speckle correlation algorithm to construct the object’s autocorrelation for imaging. Under single-colour light-emitting diode, white light, and fluorescence illumination, we experimentally demonstrate 1 μm resolution imaging and tracking of objects moving in scattering samples, while reducing the requirements for prior knowledge. We anticipate this method will enable imaging in currently inaccessible scenarios. Imaging moving objects within scattering media is important for a wide range of fields but remains challenging in many practical scenarios. Here, the authors present an overlapping speckle correlation algorithm for imaging and tracking particles in unknown media with limited prior sample knowledge.