Spectral Super-Resolution Benchmark

I want to run a rigorous, publish-ready benchmark that answers one question: do hyperspectral cubes reconstructed from RGB via Spectral Super-Resolution actually boost downstream vision performance?

Scope of the experiment
• Downstream tasks to test – Face recognition, Vehicle detection, Face anti-spoofing.
• All training and evaluation will rely on publicly available datasets; I already have a concrete shortlist I can share as soon as we begin.
• You will implement or adapt a state-of-the-art SSR network, generate the synthetic hyperspectral data, then train comparable RGB and SSR-based pipelines for each task.

Deliverables
1. Clean, well-commented code (Python, PyTorch or TensorFlow) covering data preprocessing, SSR reconstruction, task-specific model training, and evaluation.
2. Reproducible experiment scripts and environment files.
3. A technical report suitable for the methods section of a journal paper: datasets, architectures, hyper-parameters, quantitative results (accuracy/AP, ROC, EER, etc.), statistical significance tests, and ablation insights.
4. A brief slide deck that summarizes key findings and visual examples.

Acceptance criteria
• Clear and reproducible improvement (or justified lack thereof) for each task when using SSR data over plain RGB.
• All source code executes end-to-end on a fresh machine with the provided instructions.
• Figures and tables are publication-quality (vector graphics, correct captions, consistent formatting).

If you have prior experience with hyperspectral imaging, SSR, or benchmarking face and vehicle models, this should be a smooth collaboration. Let’s push the state of the art together.