Modulation transfer function in the analysis of electro-optical system performance
Bibliographic record
Abstract
Introduction/purpose: The Modulation Transfer Function (MTF) is a useful tool for an imaging system performance analysis. It is used in Electro-Optical (EO) system design, verification of targeted system parameters, but also in optimization tasks for systems under test. This methodology based on the linear systems theory allows the performance analysis of complicated EO systems to be divided into subsystems. In this paper, the MTF methodology will be presented and explained, followed by the measurements performed in the electro-optical laboratory. The MTF measurements were performed on three types of cameras spectral bands, after which the results were compared to in different the model expectations and theoretical limits for the imaging system. For one of the sensors, the limiting frequency was also measured using the USAF 1951 test target which allowed the comparison between the methods. Methods: Laboratory measurements and theoretical mathematical calculations. Results: Based on the laboratory and theoretical results, the measurement results were further analyzed. Conclusion: The measurements have proven that the calculated cutoff frequency and the MTF curve represent the limit for the real measured system performance. Therefore, this study has confirmed that the MTF can be convenient for finding system limitations and bottlenecks and for increasing the overall performance of the system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".