# Approaching Quantum Limited Super-Resolution Imaging without Prior Knowledge of the Object Location

## TL;DR

This paper introduces a multi-stage adaptive imaging method that combines traditional detection with mode-sorting to achieve near quantum-limited resolution without prior scene information, significantly reducing estimation error.

## Contribution

It proposes a novel multi-stage passive imaging strategy that dynamically allocates measurement resources to surpass classical resolution limits without prior object location knowledge.

## Key findings

- Monte Carlo simulations show 10-100x error reduction
- Adaptive two-stage scheme outperforms direct detection alone
- Method generalizes to complex imaging tasks with minimal prior info

## Abstract

A recently identified class of receivers which demultiplex an optical field into a set of orthogonal spatial modes prior to detection can surpass canonical diffraction limits on spatial resolution for simple incoherent imaging tasks. However, these mode-sorting receivers tend to exhibit high sensitivity to contextual nuisance parameters (e.g., the centroid of a clustered or extended object), raising questions on their viability in realistic imaging scenarios where little or no prior information about the scene is available. We propose a multi-stage passive imaging strategy which segments the total recording time between different physical measurements to build up the required prior information for near quantum-optimal imaging performance at sub-Rayleigh length scales. We show via Monte Carlo simulations that an adaptive two-stage scheme which dynamically allocates the total recording time between a traditional direct detection measurement and a binary mode-sorting receiver outperforms idealized direct detection alone for simple estimation tasks when no prior knowledge of the object centroid is available, achieving one to two orders of magnitude improvement in mean squared error. Our scheme can be generalized for more sophisticated imaging tasks with multiple parameters and minimal prior information.