Compressed sensing-enabled Raman spectroscopy for biomedical applications: Principles, advances, and future directions

Raman spectroscopy is a label-free optical technique widely used in biomedical analysis due to its ability to provide molecularly specific information from tissues and biofluids. Despite its strong potential for clinical diagnostics, practical implementation is often limited by weak intrinsic signal strength, long acquisition times, and the high dimensionality of spectral and hyperspectral data. Compressed sensing (CS) has emerged as an effective computational framework to address these challenges by enabling accurate signal recovery from substantially undersampled measurements, exploiting the sparsity characteristics commonly present in Raman spectra. This review systematically examines recent advances in compressed sensing-enabled Raman spectroscopy, with emphasis on underlying principles, system architectures, computational reconstruction methods, and biomedical applications.