<title>Improving spatial resolution of infrared images by means of sensor fusion</title>
Bibliographic record
Abstract
This paper presents a cooperative hierarchical fusion scheme based on `d trous' wavelet transformation for the fusion of infrared and visible images. At first, the restoration algorithm of multi-frames is presented to filter image noise and to improve the detail information of the infrared image. Then both the infrared and the visible images are decomposed to multilayer wavelet planes, respectively. A hierarchical merging is used for feature selection of wavelet planes by taking the weighted average at each scale. Lastly, the inverse wavelet transformation is implemented from the approximation data of the infrared image and the fused wavelet coefficients at various scales. One visible and three-frame infrared images are used to test the performance of the proposed scheme. Experimental results show that the spatial resolution improvement of the infrared image can be cooperatively achieved by multi-frame image restoration and sensor fusion. The advantage of the proposed cooperative merging algorithm is that the salient detail information from both visible and infrared images is preserved. No artifacts such as the blocking effect exist in the merged result. Moreover, the proposed method allows use of a dyadic wavelet to merge different sensor data of nondyadic resolution in a simple and efficient approach.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".