Adversarial and Adaptive Tone Mapping Operator for High Dynamic Range Images
Why this work is in the frame
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Bibliographic record
Abstract
This work addresses tone mapping, a common approach to convert high dynamic range (HDR) images into low dynamic range (LDR) images. We approach this problem by using adaptive tone mapping. We propose to deploy a conditional generative adversarial networks to build an adversarial and adaptive tone mapping operator (adTMO) that converts HDR into LDR images. We use an objective quality metric called the Tone Mapped Image Quality Index (TMQI) to evaluate our adTMO. Trained with 256*256 images, adTMO is able to generate 256*256 and high-resolution 1024*2048 LDR images. Given 1024*2048 HDR images, TMQI of the generated LDR images reaches the value of 0.90, which outperforms all other contemporary tone mapping operators.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it