39 000-Subexposures/s Dual-ADC CMOS Image Sensor With Dual-Tap Coded-Exposure Pixels for Single-Shot HDR and 3-D Computational Imaging
Bibliographic record
Abstract
A dual-tap coded-exposure-pixel (CEP) image sensor is presented and validated in two computational imaging applications. The NMOS-only data-memory pixel (DMP) reduces the transistor count yielding a$7{-}\mu \text {m}$pitch. One frame period can include up to 900 subexposures when operating at 30 frames/s, corresponding to 39 000 coded subexposures/s. The$320\times 320$-pixel sensor features two readout modes using column-parallel analog-to-digital converters (ADCs). ADC1 is a conventional high-accuracy$\Delta \Sigma $-modulated ADC that digitizes pixel voltage at the end of every frame period, and ADC2 is a fast energy-efficient comparator that compares the pixel voltage with a constant reference voltage during each subexposure. The outputs of the 12-bit frame-rate ADC1 and the 1-bit subexposure-rate ADC2 are adaptively combined to boost the native dynamic range of the uncoded pixel by over 57 dB, demonstrating over 101-dB dynamic range in intensity imaging. In the second demonstrated application, combined with machine-learned projected illumination patterns, the CEP camera enables single-shot structured-light 3-D imaging at the native resolution and the nominal 30 frames/s video rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".