Adversarial and adaptive tone mapping operator: multi-scheme generation and multi-metric evaluation
Bibliographic record
Abstract
Tone mapping is one of the main techniques to convert high-dynamic range (HDR) images into low-dynamic range (LDR) images. We propose to use a variant of generative adversarial networks to adaptively tone map images. We designed a conditional adversarial generative network composed of a U-Net generator and patchGAN discriminator to adaptively convert HDR images into LDR images. We extended previous work to include additional metrics such as tone-mapped image quality index (TMQI), structural similarity index measure, Fréchet inception distance, and perceptual path length. In addition, we applied face detection on the Kalantari dataset and showed that our proposed adversarial tone mapping operator generates the best LDR image for the detection of faces. One of our training schemes, trained via 256 × 256 resolution HDR–LDR image pairs, results in a model that can generate high TMQI low-resolution 256 × 256 and high-resolution 1024 × 2048 LDR images. Given 1024 × 2048 resolution HDR images, the TMQI of the generated LDR images reaches a value of 0.90, which outperforms all other contemporary tone mapping operators.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".