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Deep Learning Super-Resolution Spectrometer Based on Fiber Random Laser With Ultrahigh Spectral Purity
Jinjiang Zhao1, Xiaomei Gao1, Zilong Lu1
1School of Physics and Optoelectronic Engineering Beijing University of Technology Beijing China.
Nanophotonics (Berlin, Germany)
|March 9, 2026
Summary
This study introduces a faster super-resolution spectrometer using a fiber random laser (RL) and deep learning. It significantly reduces reconstruction time for high-resolution spectral data.
Area of Science:
- Spectroscopy
- Optical Engineering
- Applied Physics
Background:
- Super-resolution spectroscopy traditionally faces speed limitations due to extensive data acquisition.
- Random laser (RL) based methods offer enhanced spectral resolution but require numerous sub-resolution frames.
Purpose of the Study:
- To develop a deep learning super-resolution spectrometer with significantly reduced reconstruction time.
- To create a high-performance micro-nano light source for improved spectral analysis.
Main Methods:
- Utilized a fiber random laser (RL) coupled to a nested fiber microcavity to generate a high-purity micro-nano light source.
- Employed a convolutional neural network (CNN) for spectral reconstruction from sparse data.
- Reduced the number of acquired sparse frames by threefold.
Main Results:
- Achieved super-resolution spectra with up to an 80% reduction in reconstruction time.
- The novel light source exhibited high spectral purity, good directivity, and a miniature size.
- Demonstrated effective super-resolution recovery from significantly fewer raw frames or lower localization density.
Conclusions:
- The proposed deep learning super-resolution spectrometer drastically cuts acquisition time, enabling faster spectral analysis.
- This advancement promotes the development of integrated, low-cost, high-resolution, and compact spectroscopic systems.
