Enabling hyperspectral imaging in diverse illumination conditions for indoor applications
Bibliographic record
Abstract
Hyperspectral imaging provides rich information across many wavelengths of the captured scene, which is useful for many potential applications such as food quality inspection, medical diagnosis, material identification, artwork authentication, and crime scene analysis. However, hyperspectral imaging has not been widely deployed for such indoor applications. In this paper, we address one of the main challenges stifling this wide adoption, which is the strict illumination requirements for hyperspectral cameras. Hyperspectral cameras require a light source that radiates power across a wide range of the electromagnetic spectrum. Such light sources are expensive to setup and operate, and in some cases, they are not possible to use because they could damage important objects in the scene. We propose a data-driven method that enables indoor hyper-spectral imaging using cost-effective and widely available lighting sources such as LED and fluorescent. These common sources, however, introduce significant noise in the hyperspectral bands in the invisible range, which are the most important for the applications. Our proposed method restores the damaged bands using a carefully-designed supervised deep-learning model. We conduct an extensive experimental study to analyze the performance of the proposed method and compare it against the state-of-the-art using real hyperspectral datasets that we have collected. Our results show that the proposed method outperforms the state-of-the-art across all considered objective and subjective metrics, and it produces hyperspectral bands that are close to the ground truth bands captured under ideal illumination conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".