A Visible and Infrared Image Fusion Framework Based on Dual-Path Encoder-Decoder and Multi-Scale Discrete Wavelet Transform
Bibliographic record
Abstract
In recent years, extensive research has been conducted on visible and infrared image fusion (VIF) task using traditional multi-scale transform-based and deep learning model-based methods. However, there is still a need to explore the combination of neural networks and multi-scale transform. This paper proposes a novel fusion framework based on a dual-path encoder-decoder and multi-scale transform. A dual-path encoder is trained to extract rich features at different depths from source images, while a shared decoder is trained to efficiently reconstruct images from the extracted feature space. We apply the discrete wavelet transform (DWT) to generate various frequency components from the extracted features. A fusion module is utilized to achieve fusion for low and high-frequency sub-bands, respectively, which is constrained by a gradient-based fusion loss function and an absolute values maximum-selection strategy. Our proposed method is superior to current state-of-the-art fusion methods, as demonstrated through quantitative and qualitative comparisons of publicly available datasets.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".