Shivam Pande, Nicolas Nadisic, Francisco Mederos-Henry, Aleksandra Pižurica
We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ShivamP1993/ftir-unmixing | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| sample_parser | Not yet run | ShivamP1993/ftir-unmixing/unmix_cnn_wsad.py pointer only (licence: NONE) · get_code("bfbf38c764969e60") |
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Spectroscopic imaging (SI) has become central to heritage science because it enables non-invasive, spatially resolved characterisation of materials in artefacts. In particular, attenuated total reflection Fourier transform infrared microscopy (ATR-µFTIR) is widely used to analyse painting cross-sections, where a spectrum is recorded at each pixel to form a hyperspectral image (HSI). Interpreting these data is difficult: spectra are often mixtures of several species in heterogeneous, multi-layered and degraded samples, and current practice still relies heavily on manual comparison with reference libraries. This workflow is slow, subjective and hard to scale. We propose an unsupervised CNN autoencoder for blind unmixing of ATR-µFTIR HSIs, estimating endmember spectra and their abundance maps while exploiting local spatial structure through patch-based modelling. To reduce sensitivity to atmospheric and acquisition artefacts across more than 1500 bands, we introduce a weighted spectral angle distance (WSAD) loss with automatic band-reliability weights derived from robust measures of spatial flatness, neighbour agreement and spectral roughness. Compared with standard SAD training, WSAD improves interpretability in contamination-prone spectral regions. We demonstrate the method on an ATR-µFTIR cross-section from the Ghent Altarpiece by the Van Eyck brothers.
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get_code_for_paper("2603.06673")
have("2603.06673")
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