Resolution enhancement in confocal microscopy using Bessel-Gauss beams
Bibliographic record
Abstract
Laser scanning microscopy is limited in lateral resolution by the diffraction of light. We show that we can obtain twenty percent improvement in the resolution of confocal microscopy using Bessel-Gauss beams with the right pinhole size compared to conventional Gaussian beam based confocal microscopy. Advantages of this strategy include simplicity of installation and use, linear polarization compatibility, possibility to combine it with other resolution enhancement and superresolution strategies. We demonstrate the resolution enhancement capabilities of Bessel-Gauss beams both theoretically and experimentally on nano-spheres and biological tissue samples without any residual artifacts coming from the Bessel-Gauss beam side lobes with a resolution of 0.39λ. We also show that the resolution enhancement of Bessel-Gauss beams yields a better statistical colocalization analysis with fewer false positive results than when using Gaussian beams. We have also used Bessel-Gauss beams of different orders to further improve the resolution by combining them in SLAM microscopy (Switching LAser Modes : Dehez, Optics Express, 2013) achieving a resolution of 0.2λ.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".