Bibliographic record
Abstract
Image fusion is a tool for integrating a high-resolution panchromatic image with a multispectral image, in which the resulting fused image contains both the high-resolution spatial information of the panchromatic image and the color information of the multispectral image. Wavelet transformation, originally a mathematical tool for signal processing, is now popular in the field of image fusion. Recently, many image fusion methods based on wavelet transformation have been published. The wavelets used in image fusion can be categorized into three general classes: Orthogonal, Biorthogonal and Nonorthogonal. Although these wavelets share some common properties, each wavelet leads to unique image decomposition and a reconstruction method which leads to differences among wavelet fusion methods. This paper focuses on the comparison of the image fusion methods which utilize the wavelets of the above three general classes. The typical wavelets from the above three general classes – Daubechies (Orthogonal), spline biorthogonal (Biorthogonal), and A trous (Nonorthogonal) – are selected as the mathematical models to implement image fusion algorithms. When wavelet transformation alone is used for image fusion, the fusion result is often not good. However, if wavelet transform and IHS transform are integrated, better fusion results may be achieved. Because the substitution in IHS transform is limited to only the intensity component, integrating of the wavelet transform to improve or modify the intensity and the IHS transform to fuse the image can make the fusion process simpler and faster. This integration can also better preserve color information. The fusion method based on the above IHS and wavelet integration concept is employed in this paper. IKONOS image data are used to evaluate the three different kinds of wavelet fusion methods mentioned above. The fusion results are compared graphically, visually, and statistically.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".