This framework learns optimal Gaussian Optical Bandpass Filters that transform high-dimensional spectral signatures into compact, information-rich representations 🌟
Deep Gaussian Optical Bandpass Filter Design for Fermentation Index Estimation in Cocoa Beans 🔬💫
A powerful deep learning framework for learnable Optical Bandpass Filter Design! 🎯 Transform spectral signatures into meaningful insights with AI-optimized Gaussian filters 📊✨
FilterDesign is the ⭐ core ⭐ of this work!
It learns Gaussian optical filters that reduce spectral dimensionality while preserving the most discriminative features 🌟
🔬 Technical approach:
- 📡 Processes raw spectral data (hundreds to thousands of bands)
- 🧠 Learns optimal Gaussian filter parameters (μ, σ)
- ✂️ Reduces dimensionality to physically feasible spectral bands
- 🎯 Achieves state-of-the-art performance with significantly fewer spectral inputs ✨
python deep_learning.py --mode filter_design --learned-bands 6 --epochs 10python deep_learning.py --mode baseline --epochs 10python run.py # Tests 3, 6, 11 filters automatically| Mode | Description |
|---|---|
filter_design |
🌟 Primary method - Learnable Gaussian optical filters |
band_selection |
Learnable binary spectral band selection |
binary_band_selection |
Hard binary spectral band selection |
baseline |
Standard full-spectrum approach (no filtering) |
- 🧪 FilterDesign: Gaussian optical filters with learnable μ & σ
- 🤖 Multiple Backbones: SpectraNet, CNN, LSTM, Transformer, SpectralFormer
- 📈 Two-Stage Training: Joint optimization + filter parameter freezing
- 🎛️ FWHM Constraints: Bandwidth limited to physically feasible range (8.25–41.25 nm)
- 🎯 Spectral Efficiency: Drastically reduces dimensionality with minimal performance loss
- 💡 Interpretability: Highlights relevant spectral regions linked to the task
- 🔧 Modularity: Compatible with diverse neural architectures
- 📈 Validated: Demonstrated superiority over state-of-the-art band selection methods
🌸 Keep learning, keep filtering! 🌸