Single-mask sphere-packing with implicit neural representation reconstruction for ultrahigh-speed imaging
Bibliographic record
Abstract
Single-shot, high-speed 2D optical imaging is essential for studying transient phenomena in various research fields. Among existing techniques, compressed optical-streaking ultra-high-speed photography (COSUP) uses a coded aperture and a galvanometer scanner to capture non-repeatable time-evolving events at the 1.5 million-frame-per-second level. However, the use of a randomly coded aperture complicates the reconstruction process and introduces artifacts in the recovered videos. In contrast, non-multiplexing coded apertures simplify the reconstruction algorithm, allowing the recovery of longer videos from a snapshot. In this work, we design a non-multiplexing coded aperture for COSUP by exploiting the properties of congruent sphere packing (SP), which enables uniform space-time sampling given by the synergy between the galvanometer linear scanning and the optimal SP encoding patterns. We also develop an implicit neural representation-which can be self-trained from a single measurement-to not only largely reduce the training time and eliminate the need for training datasets but also reconstruct far more ultra-high-speed frames from a single measurement. The advantages of this proposed encoding and reconstruction scheme are verified by simulations and experimental results in a COSUP system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".