Algorithm Computationally Fuses Classical, Quantum Imaging

Researchers from Colorado State University and the Colorado School of Mines have developed an efficient and robust algorithm that fuses quantum and classical information for high-quality imaging. According to the researchers, the computational imaging approach is suitable for a range of imaging contexts — and will see widespread use as a result.

The approach is based on classical and quantum correlation functions obtained from photon counts, which are collected from quantum emitters illuminated by spatiotemporally structured illumination. Photon counts are processed and converted into signals of increasing order, which contain increasing spatial frequency information.

In these functions, however, the higher spatial resolution information suffers from a reduced signal-to-noise ratio at increasingly larger correlation orders.

To address the problem, the researchers developed an algorithm that they called super deconvolution imaging (SDI). The goal of this algorithm is to robustly fuse classical information, which contributes a high signal-to-noise ratio but low spatial frequency information, with quantum information, which contributes a low signal-to-noise ratio but high spatial frequency information.

“A revolution is underway in optical microscopy where the quantum properties of light are exploited to extract additional information from quantum correlations that are absent in the classical interpretation,” the researchers said in their paper.

A multi-institutional research team developed an algorithm that combines the advantages of quantum and classical imaging. The approach, the researchers said, will see widespread use because it is suitable for a range of different imaging contexts. Insets (A) and (D) use classical information only. Insets (B) and (E) combine classical and quantum information. Courtesy of Intelligent Computing (2022). DOI: 10.34133/icomputing.0003.

The researchers combined anti-bunching correlation images obtained from single quantum emitters with classical images derived from the same photon-count data. Anti-bunching is feature of single quantum emitters that can be used for superresolution imaging.

The strategy benefits from overlap in lower spatial frequency regions. “The algorithm exploits the requirement of self-consistency of the overlapping measured spatial frequency information to bootstrap the lower SNR (signal-to-noise) information at high spatial frequencies that is contributed by the quantum images,” the researchers said.

The super deconvolution imaging algorithm resulted in remarkably increased spatial frequency content, faster speed, and higher resolution, with much better mean squared errors in the reconstructed images.

According to the researchers, saturated excitation imaging, as well as various correlative microscopies, are among the example applications for which the SDI algorithm could provide advantages.

The research was published in Intelligent Computing (www.doi.org/10.34133/icomputing.0003).