4 papers
HyperBench: Standardizing and Scaling Synthetic Evaluation for Hyperspectral Super-Resolution
Ritik Shah, Marco F. Duarte
Hyperspectral super-resolution (HSR) reconstructs a high-spatial-resolution hyperspectral image by fusing a low-resolution hyperspectral image (LR-HSI) with a high-resolution multi…
Affine Subspace Models and Clustering for Patch-Based Image Denoising
Tharindu Wickremasinghe, Marco F. Duarte
Image tile-based approaches are popular in many image processing applications such as denoising (e.g., non-local means). A key step in their use is grouping the images into cluster…
SpectraMorph: Structured Latent Learning for Self-Supervised Hyperspectral Super-Resolution
Ritik Shah, Marco F Duarte
Hyperspectral sensors capture dense spectra per pixel but suffer from low spatial resolution, causing blurred boundaries and mixed-pixel effects. Co-registered companion sensors su…
SpectraLift: Physics-Guided Spectral-Inversion Network for Self-Supervised Hyperspectral Image Super-Resolution
Ritik Shah, Marco F. Duarte
High-spatial-resolution hyperspectral images (HSI) are essential for applications such as remote sensing and medical imaging, yet HSI sensors inherently trade spatial detail for sp…