Identifying Objects at the Quantum Limit for Super-Resolution Imaging

• URL: http://arxiv.org/abs/2107.00673v2
• Date: Thu, 28 Apr 2022 18:00:04 GMT
• Title: Identifying Objects at the Quantum Limit for Super-Resolution Imaging
• Authors: Michael R Grace, Saikat Guha
• Abstract summary: We analytically compute quantum-limited error bounds for hypothesis tests on any database of incoherent, quasi-monochromatic objects. We show that object-independent linear-optical spatial processing of the collected light exactly achieves these ultimate error rates.
• Score: 1.2792576041526287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider passive imaging tasks involving discrimination between known candidate objects and investigate the best possible accuracy with which the correct object can be identified. We analytically compute quantum-limited error bounds for hypothesis tests on any database of incoherent, quasi-monochromatic objects when the imaging system is dominated by optical diffraction. We further show that object-independent linear-optical spatial processing of the collected light exactly achieves these ultimate error rates, exhibiting superior scaling than spatially-resolved direct imaging as the scene becomes more severely diffraction-limited. We apply our results to example imaging scenarios and find conditions under which super-resolution object discrimination can be physically realized.

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