Reviewed by Alex SmithSep 27 2021
A glance at an object with the human eyes or a camera enables sufficient pixels of light to be gathered automatically at visible wavelengths to have a clear image of what one sees.
Comparison of single-pixel imaging, at left, and time-domain single-pixel imaging (TSPI) at right. In a typical single-pixel imaging configuration the photodiode detector has only one pixel and hence provides no spatial resolution. In TPSI, the photodiode, which lacks the temporal bandwidth to resolve ultrafast signals by itself, works as the “single-pixel” detector in the time domain and is used in conjunction with a programmable temporal fan-out gate based on a digital micromirror device. Image Credit: Jiapeng Zhao.
By contrast, researchers require highly sensitive to tools visualize a quantum object or phenomenon in which the illumination is poor or emanates from far infrared or non-visible infrared wavelengths.
For instance, the researchers have come up with single-pixel imaging in the spatial domain as an approach to pack and spatially structure as many photons as possible onto a single-pixel detector and further create an image with the help of computational algorithms.
Likewise, in the time domain, when an unidentified ultrafast signal is either weak or in the infrared or far-infrared wavelengths, there is a decrease in the potential of single-pixel imaging to visualize it.
A team of researchers from the University of Rochester has designed a time-domain single-pixel imaging method depending on the Spatio-temporal duality of light pulses. It has been explained in Optica and resolves this issue by detecting 5 femtojoule ultrafast light pulses with a temporal sampling size as low as 16 fs.
This time-domain analogy of single-pixel imaging exhibits equal benefits to its spatial counterparts: a high sensitivity, good measurement efficiency, compatibility at multiple wavelengths and sturdiness against temporal distortions.
Jiapeng Zhao, the lead author of the study and a PhD student in optics at the University of Rochester, states that possible applications include a highly precise spectrographic tool, illustrated to achieve 97.5% accuracy in determining samples utilizing a convolutional neural network with the help of this method.
According to Zhao, the technique can be further integrated with single-pixel imaging to make a computational hyperspectral imaging system. Zhao works in the Rochester research group of Robert Boyd, professor of optics.
The system has the ability to greatly expedite the detection and analysis of images at broad frequency bands. Particularly, this could be beneficial in the field of medical applications, where detection of non-visible light emanating from human tissue at different wavelengths can denote disorders like high blood pressure.
By coupling our technique with single pixel imaging in the spatial domain, we can have good hyperspectral image within a few seconds. That’s much faster than what people have done before.
Jiapeng Zhao, Study Lead Author and PhD Student in Optics, University of Rochester
The other coauthors of the study include Boyd and Xi-Cheng Zhang from Rochester, Jianming Dai from Tianjin University and Boris Braverman from the University of Ottawa.
This study was financially supported by the Office of Naval Research, the National Natural Science Foundation of China and the National Key Research and Development Program of China.
Journal Reference:
Zhao, J., et al. (2021) Compressive ultrafast pulse measurement via time-domain single-pixel imaging. Optics. doi.org/10.1364/OPTICA.431455.