Development of Weak Signal Recognition and an Extraction Algorithm for Raman Imaging.

Area of Science:

• Spectroscopy
• Biophysics
• Chemical Imaging

Background:

• Improving time resolution in Raman imaging is crucial for observing dynamic processes in interfacial catalysis and biological systems.
• Extracting weak Raman signals from strong noise under low signal-to-noise ratio (SNR) conditions presents a significant challenge.
• Current methods struggle to achieve high-quality imaging with weak signals, limiting the study of dynamic phenomena.

Purpose of the Study:

• To develop a novel signal processing algorithm for fast Raman imaging with enhanced time resolution.
• To establish a reliable method for extracting weak Raman signals under low SNR conditions.
• To determine a threshold for clear Raman imaging and optimize scanning times for trustworthy image quality.

Main Methods:

• Explored the relationship between single Raman spectrum SNR and image structural similarity (SSIM).
• Determined a semiempirical threshold (SNR = 0 dB) for clear imaging.
• Proposed a two-step algorithm: 1. SNR enhancement using Fast Fourier Transform (FFT), least squares, and 2-D median filter. 2. Local SNR evaluation for image quality prediction and clear imaging determination.

Main Results:

• Successfully identified a threshold of SNR = 0 dB for clear Raman imaging.
• Developed and validated a signal processing algorithm for fast Raman imaging.
• Demonstrated the algorithm's effectiveness in fast imaging of cells under low SNR conditions.

Conclusions:

• The proposed algorithm enables reliable extraction of target Raman signals under low SNR.
• The method allows for the determination of optimal scanning times to achieve trustworthy Raman image quality.
• This advancement significantly improves the time resolution of Raman imaging for dynamic process observation.