Motion-free high-resolution on-chip microscopy using LED matrix
Bibliographic record
Abstract
Lensless microscopy is an imaging technique that allows high-resolution imaging over a large field of view with a cost-effective design. Conventional lensless microscopy often utilizes multi-height phase retrieval and pixel-super-resolution algorithms to reconstruct high-resolution images, requiring mechanical stages for three-dimensional relative movements between a light source, camera, and sample. However, the excessive use of stages inevitably increases the bulkiness of the system and extends the image acquisition time. Here, we propose a motion-free lensless microscope that incorporates an RGB LED matrix array. A high-resolution holographic image is reconstructed from subpixel-shifted color images obtained with LED illuminations without any mechanical movement. Using a prototype system, we have demonstrated a spatial-bandwidth product of 30 megapixels with a resolution of 0.87 µm and a field of view of 24 mm 2 . The usability of the proposed method has been further tested for histopathologic examination. Our system features a compact and high-performance design with inexpensive optoelectronic elements, a conventional CMOS sensor and an LED matrix, which are well-aligned with the original design motivation of lensless imaging methods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".