Infrared visible image fusion algorithm based on double branching and resultant decomposition
Why this work is in the frame
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Bibliographic record
Abstract
Currently, visible and infrared image fusion (VIF) technology has a wide range of applications in road safety monitoring, anti-surveillance, etc.However, the traditional image fusion algorithms in the feature fusion process will have limitations such as part of the information is lost, etc.For this reason, this paper proposes an infrared visible image fusion algorithm based on the double-branching and decomposition of the results.The algorithm irstly adopts the dense block method, extracts visible image features, and uses a feature pyramid network to extract infrared features.The algorithm irstly adopts the dense block method to extract the visible image features, and uses the feature pyramid network to extract the infrared features, then, based on the deep learning network structure to extract the image information of different modalities, and designs the fusion network constrained by the three loss functions of the gradient loss, intensity loss and decomposition loss, so as to obtain a good fusion effect of the image.The experimental results show that the proposed algorithm achieves the optimal value in ive indexes, and reaches sub-optimal value in one index, indicating that the proposed algorithm fuses the images with the optimal value and sub-optimal value.At the same time, the proposed algorithm retains the main thermal radiation information of infrared images better than other algorithms such as DenseFuse and IFCNN, which is superior to some extent.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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