Extended-object reconstruction in adaptive-optics imaging: the multiresolution approach
Bibliographic record
Abstract
\n Aims. We propose the application of multiresolution transforms, such as wavelets and curvelets, to reconstruct images of extended objects that have been acquired with adaptive-optics (AO) systems. Such multichannel approaches normally make use of probabilistic tools to distinguish significant structures from noise and reconstruction residuals. We aim to check the prevailing assumption that image-reconstruction algorithms using static point spread functions (PSF) are not suitable for AO imaging.\n Methods. We convolved two images, one of Saturn and one of galaxy M100, taken with the Hubble Space Telescope (HST) with AO PSFs from the 5-m Hale telescope at the Palomar Observatory and added shot and readout noise. Subsequently, we applied different approaches to the blurred and noisy data to recover the original object. The approaches included multiframe blind deconvolution (with the algorithm IDAC), myopic deconvolution with regularization (with MISTRAL) and wavelet- or curvelet-based static PSF deconvolution (AWMLE and ACMLE algorithms). We used the mean squared error (MSE) to compare the results.\n Results. We found that multichannel deconvolution with a static PSF produces generally better results than the results obtained with the myopic/blind approaches (for the images we tested), thus showing that the ability of a method to suppress the noise and track the underlying iterative process is just as critical as the capability of the myopic/blind approaches to update the PSF. Furthermore, for these images, the curvelet transform (CT) produces better results than the wavelet transform (WT), as measured in terms of MSE. \n
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".