Bayesian-Approach-in-Learning-Based-HSI-Denoising

Model Architecture

Bayesian Motivated Training

$\bg_black \begin{equation}\label{eq:5} \begin{cases} \underset{\theta}{\text{min}}~\mathcal{L}(\hat{f},f)\\ \text{such that } \hat{f}=\underset{f}{\text{arg min}} \lVert h-f\rVert_p+ \lambda\mathcal{R}(f;\theta)\\ \end{cases} \end{equation}$

Training and Test Data

Washington DC Dataset: https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html

ICVL Dataset: http://icvl.cs.bgu.ac.il/hyperspectral/

CAVE dataset: https://www.cs.columbia.edu/CAVE/databases/multispectral/

Cite the paper with:

Aetesam, Hazique, Suman Kumar Maji, and Hussein Yahia. "Bayesian Approach in a Learning-Based Hyperspectral Image Denoising Framework." IEEE Access 9 (2021): 169335-169347.