A Frequency Decomposition and Gaussian-Based Enhancement Network for Infrared and Visible Image Fusion
Bibliographic record
Abstract
The purpose of infrared image and visible image fusion is to preserve information in different modalities. In order to solve the redundancy of modal frequency domain information extraction and feature mapping, we propose a frequency decomposition and Gaussian-Based enhancement network for infrared and visible image fusion. Firstly, we design a frequency decomposition convolution, which divides the feature map to realize the independent modeling of different frequency information, so as to extract the deep-level features more accurately. In addition, we design enhancement module combined with Gaussian filter to enhance the feature expression and optimize the loss function. Finally, we introduce dual-discriminators to refine the differentiation of infrared and visible images, significantly enhancing global information expression and detail presentation in fused image. Experimental outcomes demonstrate that our fusion method can effectively integrate the dominant information of the two images. Notably, our method outperforms other advanced fusion algorithms by enhancing the performance of object detection tasks, particularly in terms of improving the accuracy of detecting cars and pedestrians.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".