Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning for Retinal Vessel Inpainting
Bibliographic record
Abstract
Inpainting is an ill-posed inverse problem that uses information from the available parts of the image to fill in the missing parts. Among the approaches used to solve this problem, recent works tend toward sparse representation algorithms. In particular dictionary learning for sparse representation of signals is preferred in this application. A recent work utilized recursive least squares dictionary learning (RLS-DL) to improve the inpainting of retinal vessels and has shown superiority over the existing approaches. In this paper, we propose to use generalized adaptive weighted recursive least squares (GAW-RLS) dictionary learning to inpaint the retinal vessels. The proposed GAW-RLS applies a correction weight to adaptively control the relative consistency of the training data with the existing estimate of the dictionary per each iteration. This enables GAW-RLS to outperform RLS-DL in vessel inpainting. Our simulation results indicate advantages of GAW-RLS over RLS-DL by reducing the recovery error for the missing pixels.
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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 | 0.000 |
| 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".