Fringe Image Enhancement for Structured Light 3-D Measurement of Low-Reflective Objects
Bibliographic record
Abstract
3-D measurement of low-reflective objects presents significant challenges in industrial inspection and diagnostics. This article introduces a new fringe image enhancement method for structured light 3-D measurement, tailored specifically for low-reflective objects. A single-channel Transformer Retinex (SCTR) enhancement network is designed for processing fringe images. The SCTR network’s framework integrates the Retinex model with Transformer blocks and incorporates a novel modulation loss function tailored for the preservation and enhancement of fringe texture details. Since the network training process requires extensive paired datasets, we have created a digital twin of the structured light system to produce a large virtual dataset, supplemented by real datasets for deep learning. With the developed stereo structured light system, accurate 3-D measurement of low-reflective objects was accomplished. Experimental results demonstrated that the proposed method outperforms state-of-the-art techniques in both image quality enhancement and 3-D measurement accuracy, providing an efficient solution for measuring low-reflective objects.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".