Infrared visible image fusion algorithm based on double branching and resultant decomposition
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".