Capturing ultrafast transient phenomena conventionally requires streak cameras or computational imaging based on compressed sensing, which lead to complex and costly systems. In this Letter, we demonstrate, to the best of our knowledge, the first fully passive single-shot ultrafast imaging architecture assembled entirely from off-the-shelf, low-cost components. A commercial microlens array combined with a stack of standard microscope cover glasses maps temporal information into multiple spatial channels, and a consumer-grade CMOS image sensor records all delayed replicas within a single camera exposure. The proposed system has a total hardware cost below US$500 and captures the evolution of a picosecond laser pulse with a temporal sampling interval of 1.46 ps, an effective frame rate of 685 Gfps, and a sequence depth of ten frames. The temporal fidelity of the system is verified by recovering the expected Gaussian pulse profile, and the spatial resolution is characterized through a point-source measurement with a point spread function of 1.86 and 1.62 pixels full width at half maximum along the horizontal and vertical directions, respectively. The proposed architecture presents an alternative approach to single-shot ultrafast imaging with a simple, low-cost, computation-free, and fully passive design.
Schematic of the proposed low-cost passive spatially multiplexed ultrafast imaging system. A microlens array generates replicated image channels, each of which experiences a different optical delay introduced by a stack of standard microscope cover glasses. Temporally delayed replicas are simultaneously recorded within a single camera exposure using a consumer-grade CMOS sensor.
Single-shot reconstruction of the temporal evolution of a picosecond laser pulse. Each sub-image corresponds to a different optical delay introduced by the proposed spatial multiplexing architecture. The sequence is recovered from a single camera exposure with a temporal spacing of 1.46 ps between the frames.
Normalized total intensity extracted from reconstructed frames as a function of relative temporal delay. The measured temporal profile follows a Gaussian distribution (dashed curve), which confirms the accurate preservation of the pulse dynamics.

How do they get back the temporal information? I.e. go reverse on equation 2 of their paper?
ReplyDeleteEvery sub-image of the sensor captures the time-integrated information, which erases all information about the ultrafast dynamics. They give no explanation about that part at all.
Seems like a bogus paper...
If you integrate a very short pulse of duration \tau over T_exp >> \tau, with various delays D_i < tau, then the integrals will be equal for all D_i.
ReplyDeleteYou end up with NxN identical images after the multiplexer ... there is no way to recover anything.
The only way to end up with different images would be an overlap of the short pulse window with the start of the exposure window T_0, or the trailing edge T_0+T_exp ...ignoring pixel to pixel jitter of this edge ...
The reconstruction latency is actually a feature. by over-sampling the noise floor’s sparsity, we achieve a negative SNR that captures photons before they even arrive.
DeleteIt’s not a bandwidth bottleneck, it’s just temporal aliasing disguised as a budget constraint
"we achieve a negative SNR that captures photons before they even arrive"
Deletewhat does that even mean?
It seems you are not up to date with the latest and most cutting edge technology. Although counterintuitive, this phenomenon is entirely consistent with modern panendermic ultrafast imaging theory, particularly in systems employing temporal inverse semi-boloid reconstruction. Negative SNR should therefore not be interpreted as “less than no signal,” but rather as evidence that the imaging manifold has entered a pre-luminous computational state in which anticipated photons contribute to the measured spurving statistics prior to physical detection.
Deletefunny
DeleteI understand yoir objection, but it arises from using first-order sanity-preserving mathematics.
DeleteIn second-order Hilbert topology, the measured image is not reconstructed from photons, but from the cohomology class of photons that the detector would have preferred to see.
This is pre-validated computational luminance.