Image Reconstruction Combined With Interference Removal Using a Mixed-Domain Proximal Operator
Why this work is in the frame
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Bibliographic record
Abstract
In certain imaging systems, frames of acquired raw data are preprocessed with a filtering stage before being processed with an image reconstruction stage. During these sequential stages, distortion may arise if valid signal cannot adequately be distinguished from interference. To avoid distortion, a mixed-domain proximal operator is mathematically formulated, which permits interference to be separated from data concurrently during a combined processing stage. The technique is demonstrated with an application involving optoacoustic imaging. Reconstruction is performed with an accelerated proximal gradient method. Total-variation minimization is used to promote smoothness in the image domain. In the data domain, interference is modeled as a low-rank matrix, which corresponds to a few time dependent interference components being coupled to each channel by determined amounts. Results are presented that demonstrate the ability to separate interference on a digital phantom used to simulate optoacoustic signals.
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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 it