Fourier Single Pixel Imaging via Spatial-Temporal 3D Joint Priors
Bibliographic record
Abstract
Single-pixel devices excel in imaging non-visible wavebands and extreme conditions but struggle with low spatial and temporal resolution, particularly at low sampling rates. Fourier single-pixel imaging (FSI) uses sinusoidal structured illumination to capture an image’s Fourier spectrum, allowing high-quality reconstructions with fewer measurements. However, traditional FSI combined with total variation (TV) regularization often results in overly smooth images with staircase artifacts, especially with aggressive undersampling. To address these challenges, we propose a novel 3D Hessian-Based FSI reconstruction framework that extends the 2D Hessian regularizer into the spatial-temporal domain, leveraging inter-frame redundancy. Our method integrates a second-order smoothness prior with a local low-rank temporal constraint, implemented through a plug-and-play ADMM algorithm. Simulation results show significant improvements, achieving about 5 dB PSNR gain at just a 10% sampling rate, while preserving fine spatial details and avoiding staircase artifacts. This approach combines theoretical rigor with practicality, making it suitable for real-time single-pixel video imaging under extreme undersampling conditions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".