Dual-Port CMOS Image Sensor with Regression-Based HDR Flux-to-Digital Conversion and 80ns Rapid-Update Pixel-Wise Exposure Coding
Bibliographic record
Abstract
Today's best consumer cameras typically use computational imaging techniques to digitally enhance images by means of software post-processing to yield both high fidelity and low cost. A common technique, for example, is to combine multiple shots with different camera settings into one enhanced image that has a high dynamic range (HDR). This post-processing-based approach fails when fast motion and/or rapidly changing illumination are present, as often happens in autonomous driving, drone imaging, and action-camera applications, or when the illumination itself is actively controlled (e.g., for depth sensing). These applications require a much tighter temporal integration of: (1) in-pixel processing, (2) pixel readout, and (3) post-capture enhancement. Aiming to address these needs, a new class of ‘coded’ computational image sensors has emerged, with both fine (i.e., per-pixel) [1–3] and coarse (i.e., per-pixel-cluster) [4] programmable exposure control. Some of these sensors offer spatial exposure control for single-shot HDR imaging [1], [2], but require multiple ADC types and/or a number of pre- and postprocessing steps (e.g., adaptive pixel-wise exposure control, HDR reconstruction, etc.). Other coded sensors support a variety of computational imaging techniques (e.g., robust depth imaging, compressed sensing [3]), but their conventional ADCs do not offer HDR readout. HDR sensors exist that digitize the pixel output during exposure, before it saturates, but offer no coding [5], [6].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".