Perspectives and recent advances in super-resolution spectroscopy: Stochastic and disordered-based approaches

Abstract

Spectroscopic applications are characterized by the constant effort to combine high spectral resolution with large bandwidth. A trade-off typically exists between these two aspects, but the recent development of super-resolved spectroscopy techniques is bringing new opportunities into this field. This is particularly relevant for all applications where compact and cost-effective instruments are needed such as in sensing, quality control, environmental monitoring, or biometric authentication, to name a few. These unconventional approaches exploit several strategies for spectral investigation, taking advantage of concepts such as sparse sampling, artificial intelligence, or post-processing reconstruction algorithms. In this Perspective, we discuss the main strengths and weaknesses of these methods, tracing promising future directions for their further development and widespread adoption.