Super-Resolution Sensing and Imaging Using Structured Light

Justin A Patel, Purdue University


Optical imaging methods are limited by the wavelength of light that they use and the amount of scatter that must be imaged through. Super-resolution imaging and sensing methods are those that bypass or mitigate such restrictions. Two super-resolution approaches are presented here using spatially or temporally structured light. Temporal intermittence or blinking of fluorescent emitters is exploited for localization through significant depths of heavy scatter to high resolution, and an efficient algorithm for doing so is presented. Such temporal structure of emission allows far greater resolution than previous comparable imaging methods, providing opportunities in biophotonics and environmental sensing. Spatial structure can be imposed on coherent light that passes through a heavily scattering medium, in the form of a speckle pattern. Speckle intensity correlations are sensitive to the motion of a moving object obscured by scatter, and we demonstrate that this scatter can act as an analyzer, enhancing this sensitivity as the amount of scatter increases. This increased sensitivity is studied using random matrix theory, and eigenchannel analysis is proposed as an explanation. Simulations demonstrate that a randomly scattering analyzer can give sub-wavelength geometric information about a translated, hidden object. Relative motion of structured illumination is explored, with simulations and mathematical analysis demonstrating far-subwavelength sensitivity using moving fields with multiple different types of structure. This work could enable a new approach for material inspection and characterization, and provide improvements in microscopy.




Webb, Purdue University.

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