Deep learning as phase retrieval tool for CARS spectra.

Area of Science:

• Optical Physics
• Spectroscopy
• Computational Science

Background:

• Phase extraction in optical measurements is a persistent challenge.
• Algorithmic methods offer efficient, fast, and easily implemented alternatives to complex optical setups.
• Accurate phase information is vital for interpreting spectroscopic data.

Purpose of the Study:

• To investigate and compare the efficacy of different phase retrieval algorithms.
• To introduce and evaluate deep learning, specifically Long Short-Term Memory (LSTM) networks, for phase retrieval.
• To assess the performance of LSTM against established methods for coherent anti-Stokes Raman spectra.

Main Methods:

• Investigated three phase retrieval techniques: Maximum Entropy (MEM), Kramers-Kronig (KK) relations, and Long Short-Term Memory (LSTM) deep learning.
• Applied these methods to phase retrieval in optical measurements, focusing on coherent anti-Stokes Raman spectra.
• Benchmarked the performance of LSTM against MEM and KK.

Main Results:

• Long Short-Term Memory (LSTM) networks demonstrated superior performance in phase retrieval.
• LSTM significantly outperformed both Maximum Entropy (MEM) and Kramers-Kronig (KK) methods.
• The study highlights the potential of deep learning for complex spectroscopic data analysis.

Conclusions:

• Deep learning, particularly LSTM, presents a highly effective approach for phase retrieval in optical measurements.
• LSTM offers a significant advancement over traditional MEM and KK methods for coherent anti-Stokes Raman spectra.
• This research paves the way for more efficient and accurate spectroscopic data analysis using AI.