Lightweight Infrared and Visible Image Fusion Technique: Guided Gradient Optimization Driven
Bibliographic record
Abstract
Infrared and visible image fusion technology aims to combine data from several spectral bands in order to increase target identification, processing capabilities, and image quality. With the rapid development of consumer electronic products for imaging, there is an urgent need for a lightweight and efficient fusion technology that ensures efficient information extraction and fusion while maintaining image quality. Existing algorithms designed to achieve accurate information extraction, noise reduction, artefact suppression, and edge preservation need to be simplified and more challenging to meet the requirements of lightweight imaging consumer electronic products. We propose a lightweight method for the fusion of infrared and visible images by exploiting the properties of the Anisotropic Guided Filter and the Gradientlet Filter. This method achieves significant feature texture extraction, effectively reduces gradient texture and noise, minimizes halo artifacts, and enhances edge contours while preserving overall image brightness and edge gradients. Furthermore, the explicit stage processing and concise algorithmic structure design of this method contribute to its optimal time efficiency. Experimental results demonstrate its superiority in both subjective visual effects and objective metrics over nine other existing image fusion methods.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".