BAARC reconstructs full Raman spectra from partial adaptive measurements
Original title: BAARC: Uncertainty-Driven Adaptive Partial Spectral Measurement to Full Reconstruction for Material Classification
A simulated Bayesian framework picks small wavenumber patches until reconstruction and classification uncertainty are low.
Researchers simulate BAARC, a jointly trained two-level framework for Raman spectroscopy when instruments cover limited wavenumbers. Masked training drives laser channel selection from progressive partial measurements of small patches. At each step the model reconstructs the full spectrum and classifies the material, stopping when uncertainty is low. Evaluations report high-accuracy identification using only a fraction of full-spectrum channels.
- BAARC uses masked training to learn features that select the next laser channel.
- Reconstruction uncertainty chooses the next patch; classification uncertainty ends measurement.
- The authors say the loop reduces physical measurement steps while keeping spectral fidelity.
- Results are from simulated experimental evaluations, not a deployed instrument.
From the original article
1 min read at arxiv.orgSpectroscopic instruments with limited wavenumber coverage require accurate scan positioning or multiple measurements to complete the material analysis. This often requires prerequisite knowledge about the material under diagnosis to measure the spectral regions with distinguishing peaks.
By Adithya Ashok Chalain Valapil · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.