Variational Textured Image Decomposition with Improved Edge Segregation
Bibliographic record
Abstract
Image decomposition consists of splitting an image into two or more components. One component is piecewise smooth and models object shapes. Another component consists of the texture in the image, possibly including some noise. Image decomposition is useful for a host of image processing tasks, e.g. texture segmentation and image inpainting. In this paper, we consider ways of improving both the speed and quality of image decomposition using the basic variational approach of Meyer (2001) by adding extra regularization terms. A measure of quality of image decomposition found in the literature Daubechies, I et al., (2004) is the absence of cartoon edges in the texture component of the decomposition. In this paper, we introduce a method called improved edge segregation image decomposition, which ensures this quality measure is high. When combined with active contour texture discrimination, improved results are obtained over conventional methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".