Multi-Exposure Image Fusion by Optimizing A Structural Similarity Index
Bibliographic record
Abstract
We propose a multi-exposure image fusion (MEF) algorithm by optimizing a novel objective quality measure, namely the color MEF structural similarity (MEF-SSIMc) index. The design philosophy we introduce here is substantially different from existing ones. Instead of pre-defining a systematic computational structure for MEF (e.g., multiresolution transformation and transform domain fusion followed by image reconstruction), we directly operate in the space of all images, searching for the image that optimizes MEF-SSIMc. Specifically, we first construct the MEF-SSIMcindex by improving upon and expanding the application scope of the existing MEF-SSIM algorithm. We then describe a gradient ascent-based algorithm, which starts from any initial point in the space of all possible images and iteratively moves towards the direction that improves MEF-SSIMcuntil convergence. Numerical and subjective experiments demonstrate that the proposed algorithm consistently produces better quality fused images both visually and in terms of MEF-SSIMc. The final high quality fused image appears to have little dependence on the initial image. The proposed optimization framework is readily extensible to construct better MEF algorithms when better objective quality models for MEF are available.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".