A 30-fps 192 × 192 CMOS Image Sensor With Per-Frame Spatial-Temporal Coded Exposure for Compressive Focal-Stack Depth Sensing
Bibliographic record
Abstract
In this article, we present a CMOS image sensor (CIS) for coded-exposure-based compressive focal-stack imaging. The proposed CIS has a pixel design, which includes two capacitive trans-impedance amplifiers (CTIAs) and a static random access memory (SRAM), and is capable of per-frame exposure encoding with adjustable spatiotemporal resolutions. A proof-of-concept CIS prototype with a 192$\times $192 pixel array is designed and fabricated in a 0.13-$\mu \text{m}$CMOS process with a pixel size of 12.6$\times $12.6$\mu \text{m}^{2}$. Operating at 30 frames per second (fps), the CIS demonstrates spatial–temporal coded exposure at a maximum rate of 768 masks/frame. The column-wise 10-bit single-slope (SS) analog-to-digital converter (ADC) includes a ramp-slope adaptation feature used for power optimization. During a frame of coded exposure, a linear focal sweep is implemented by a voice-coil motor (VCM) lens mounted in front of the proposed CIS. Through the sparse reconstruction of the coded image, a focal stack consisting of a volume of defocused images is used to synthesize the scene depth map. By introducing coded exposure, the proposed on-chip compressive focal-stack imaging approach facilitates a frame-saving method for passive depth sensing in machine vision and other imaging applications.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".