SEDRFuse: A Symmetric Encoder–Decoder With Residual Block Network for Infrared and Visible Image Fusion
Bibliographic record
Abstract
Image fusion is an important task for computer vision as a diverse range of applications are benefiting from the fusion operation. The existing image fusion methods are largely implemented at the pixel level, which may introduce artifacts and/or inconsistencies, while the computational complexity is relatively high. In this article, we propose a symmetric encoder-decoder with residual block (SEDRFuse) network to fuse infrared and visible images for night vision applications. At the training stage, the SEDRFuse network is trained to create a fixed feature extractor. At the fusing stage, the trained extractor is utilized to extract the intermediate and compensation features, which are generated by the residual block and the first two convolutional layers from the input source images, respectively. Two attention maps, which are derived from the intermediate features, are then multiplied by the intermediate features for fusion. The salient compensation features obtained through elementwise selection are passed to the corresponding deconvolutional layers for processing. Finally, the fused intermediate features and the selected compensation features are decoded to reconstruct the fused image. Experimental results demonstrate that the proposed fusion solution, i.e., SEDRFuse, outperforms the state-of-the-art fusion methods in terms of both subjective and objective evaluations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".