Novel Real-Time Tone-Mapping Operator for Noisy Logarithmic CMOS Image Sensors
Bibliographic record
Abstract
Logarithmic CMOS image sensors are easily able, at video rates, to capture scenes where the dynamic range (DR) is high. However, tone mapping is required to output resulting images or videos to standard low-DR displays. This article proposes a new method, designed especially for logarithmic CMOS image sensors, which can suffer from temporal, and residual fixed pattern, noise. The novel tone mapping, a global operator based on histogram adjustment, uses a model of the camera noise to ensure that the mapping does not amplify the noise above a display threshold. Moreover, to reduce the likelihood of flickering, a temporal adaptation process is incorporated into the histogram calculation. Furthermore, to reduce complexity for real-time processing, a fixed-point implementation is designed for the proposed tone mapping. The novel operator and its fixed-point design are validated through offline and real-time experiments with a logarithmic CMOS image sensor. © 2016 Society for Imaging Science and Technology. [DOI: 10.2352/J.ImagingSci.Technol.2016.60.2.020404]
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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".