Direct Noise-Resistant Edge Detection with Edge-Sensitive Single-Pixel Imaging Modulation
Bibliographic record
Abstract
The majority of edge detection methods are applied after the capture of object photos. Thus, edge detection quality suffers when disturbances occur during imaging. This work proposes an effective edge detection technique for single-pixel imaging (SI). A sequence of edge-sensitive single-pixel imaging (ESI) and single-round edge-sensitive single-pixel imaging (SESI) modulation patterns is specially designed to extract the edges of unknown objects directly without the need for any previous images. The modulation patterns are formed by convolving the SI basis patterns with a second-order differential operator. Compared with existing published edge detection methods, experimental results revealed that the proposed SESI increased the signal-to-noise ratio by at least 228%, thereby reducing the edge detection time by at least half. The edge detection performance of the SESI scheme was also demonstrated on moving objects, with SESI detecting clear edges even when the target was in motion. Moreover, unlike traditional methods, ESI and SESI are immune to light interference and can detect clear edges of objects even if the objects are corrupted by severe interference from laser or light-emitting diode light sources, whereas traditional methods exhibit substantial noise contamination. Consequently, ESI and SESI can lay the groundwork for fast and robust edge detection operations without imaging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".