Imaging through strong external illumination via mode-error analysis

In optical imaging, various techniques have been studied to mitigate unwanted light. Mode-selective detection, which filters out unwanted light modes, is widely used to reduce the impact of external light. However, this technique cannot completely reject external light when it shares the same mode as the desired light. In this paper, we analyze an image reconstruction method, called mode-error-based imaging (MEI), inspired by a recent study on quantum-secured imaging, to reject image corruption from external environments, including noise. MEI constructs a target image using an estimated reconstruction factor obtained by analyzing the source light mode and the measured light mode. We simulate the capacity and provide a proof-of-principle demonstration of MEI. We investigated the conditions for enhancing image quality under external illumination with MEI compared to the mode-selective method. We anticipate that existing post-processing techniques for noise reduction, such as machine learning, can also be used in parallel to improve image quality. The noise reduction capability of MEI can be utilized not only in imaging systems with strong background noise, such as microwave-based imaging, but also in imaging systems with weak probes, such as bioimaging.