Hyperspectral Image Denoising Using Unfolding Graph Regularization and Hybrid Total Variation
Bibliographic record
Abstract
Hyperspectral image (HSI) denoising is a critical preprocessing step in numerous HSI applications. However, existing model-based and deep learning methods struggle to balance model complexity and interpretability. To address local feature modeling and parameter scale control, this paper proposes a hyperspectral image denoising method based on deep algorithm unfolding with Laplacian regularization and spatial-spectral hybrid total variation. Specifically, we combine graph Laplacian regularization with hybrid spatial-spectral total variation to jointly model the local features and global spatial-spectral characteristics of HSI. The deep unfolding network architecture, based on deep algorithm unfolding, leverages the layer independence and stable convergence of the ADMM iterations, enabling the construction of an end-to-end network model with a small number of parameters. Experimental results on both simulated and real datasets demonstrate that the proposed model achieves significant performance improvements in HSI denoising compared to existing techniques.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".