Superresolution Line Scan Image Sensor for Multimodal Microscopy
Bibliographic record
Abstract
A low-cost contact scanning microscope is presented which performs optical imaging of millimeter-scale samples with multiple sensory modalities at a spatial resolution better than the pixel size in both x and y dimensions. The 7.5 mm 3.2 mm 0.35 m CMOS image sensor is comprised of 214 scanning lines of 256 pixels, each line horizontally shifted by 300 nm with respect to the adjacent lines. When scanning in the y dimension, this results in a staircase-like staggered-pixels organization with an effective spatial resolution in the x dimension of less than the pixel size, with a theoretical limit of 300 nm, subject to the light diffraction limit and to photodiode size-dependent spatial aliasing. The height of the resulting pixel "staircases" is capped at 2.5 mm by wrapping the 215th row back to the first row, yielding an approximately 2 mm 2.5 mm instantaneous scanning window size. The spatial resolution in the y dimension is set by the sample scanning rate and the frame rate, subject to the same limitations. Integration of multiple scanning lines naturally lends itself to the inclusion of multiple sensory modalities, with five modalities included as an example: High-resolution (up to 300 nm), fluorescence-sensitive, and triple-orientation light polarization-sensitive pixels. The resulting modified scanning pattern is digitized by on-chip column-parallel 2nd order Delta-Sigma ADCs with ENOB of 9.1 and is reconstructed into a full-resolution image in software. Experimental measurements, where contact-scanning is emulated by the sample image moving on an LCD monitor and projected through a lens, support the validity of the presented concept.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".