Minimal Capture Sets for Multi-Exposure Enhanced-Dynamic-Range Imaging
Bibliographic record
Abstract
Multi-exposure enhanced-dynamic-range imaging (MEDRI) is a common technique for overcoming the limited dynamic range of digital cameras. In MEDRI, the target scene is captured multiple times at different camera exposure settings. The resulting set of low-dynamic range images is then combined using software to create a single high-dynamic-range digital image of the target scene. In this paper we describe a general analytical framework for MEDRI systems. Based on this framework, we then present an optimal greedy algorithm for finding the minimum number of exposures required to capture the entire dynamic range of any MEDRI system. The resulting set of exposures is often significantly smaller than the entire set of available camera exposure settings, and therefore represents a significant speed-up in the HDR-image-acquisition process. We demonstrate this benefit via a case study. For the system considered, we show that minimal exposure set found via the optimal algorithm reduces the number of required LDR exposures from 16 to 3
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".