Multiresolution region-based image fusion using the Contourlet Transform
Bibliographic record
Abstract
Different sensors provide a variety of images with different specifications (spectral, spatial and radiometric resolution, etc.). Image fusion techniques have been utilized to benefit the best features of all input images and to provide better application-wise output images. In this paper, a new region-based image fusion technique using the Contourlet Transform (CT) is proposed to produce a fused image better for human and machine interpretation and to reduce the computational effort of the traditional techniques. Due to the high directionality and anisotropy of the CT, the proposed technique is mainly developed to solve the problem of capturing the fine lines and contours of the input images. In this technique, the input images are segmented into small regions more suitable for the proposed fusion approach, where the segmentation process is performed in the frequency domain for better results. The fusion decision is made based on a new quality assessment scheme for each segmented region. Also, the presented region-based fusion approach is more robust than the traditional pixel-based techniques, where it reduces: the blurring effects, sensitivity to the misregistration, and noise effect in remote sensing images.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".