High-Speed Delay Scanning and Deep Learning for Fingerprint SRS Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing SRS microscopy systems face limitations in achieving high-speed, high-spectral resolution, and high-signal-to-noise ratio (SNR) imaging, particularly in the fingerprint region, due to physical constraints and the need for improved computational methods that can bypass model design.
Innovation Solution
Implementing a high-speed delay scanning assembly with a stepwise reflective surface and a fast linear scanner, combined with a trained encoder-decoder convolution neural network (CNN) for image restoration, specifically designed for spectroscopic SRS images, to enhance spectral and spatial domain processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If high-speed delay scanning is implemented to achieve fast imaging, then imaging speed is improved, but spectral resolution deteriorates
Solution Approach 1:
The system dynamically adjusts the delay range between pump and Stokes beams using a programmable delay stage, allowing the delay to be varied during acquisition to optimize both speed and spectral resolution for different imaging conditions
Solution Approach 2:
The patent changes key parameters including delay range (from fixed to tunable), pixel dwell time (optimized for speed), and spectral focusing parameters to achieve high-speed imaging while maintaining adequate spectral resolution through computational correction
2Measurement precision
If narrowband lasers are used for single vibrational mode excitation, then spectral resolution is improved, but chemical specificity deteriorates
Solution Approach 1:
The system uses broadband lasers that can excite multiple vibrational modes simultaneously, making the system universal for detecting various chemical species with different Raman-active vibrations, thereby improving chemical specificity while maintaining spectral resolution through spectral focusing and computational methods
3Adaptability or versatility
If spectroscopic SRS is implemented to acquire Raman spectrum at each pixel, then chemical specificity is improved, but imaging speed deteriorates
Solution Approach 1:
The system uses pulsed laser excitation with periodic timing, where pump and Stokes pulses are delivered in synchronized cycles with optimized timing to acquire spectral information at each pixel while maintaining high imaging speed through efficient pulse utilization
Solution Approach 2:
The delay between pump and Stokes beams is dynamically adjusted during spectral acquisition to sweep through the Raman spectrum, enabling spectroscopic information gathering at each pixel without requiring mechanical scanning, thus maintaining imaging speed
4Reliability
If deep learning techniques are applied for image restoration, then SNR is improved, but computational complexity increases
Solution Approach 1:
The deep learning model is pre-trained on simulated and experimental data to learn the relationship between low-SNR and high-SNR images, so that during actual imaging, the pre-trained model can be applied directly for fast SNR improvement without requiring complex real-time computations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables high-fidelity fingerprint spectroscopic SRS imaging with improved spectral resolution, SNR, and tunable delay ranges, achieving results comparable to longer pixel dwell times without model-based simplifications.
Implementation Method 1
The scanner is configured to repeatedly scan a first pulsed interrogation beam along a scan line across the stepwise reflective surface in a Littrow configuration. This scanning changing a path distance of the first beam (with each step of the stepwise reflective surface) thereby introducing a sequence of varying temporal delays relative to a second pulsed interrogation beam
Implementation Method 2
whereby the first interrogation beam is combined with the second pulsed interrogation beam and linearly chirped by a high dispersion medium (e.g., high dispersion glass rods) to temporally separate different frequency components prior to sample interrogation
Implementation Method 3
When the difference in frequency between pump and stokes photons Δω=ωp−ωS is equal to a particular Raman-active molecular vibration of the sample, SRS signals equivalent to changes in the intensity of the pump and Stokes beams (including both stimulated Raman loss (SRL) and stimulated Raman gain (SRG)) are generated due to the nonlinear interaction between the photons and the molecules
Data Source
AI summary
Systems and methods implement of high-speed delay scanning for spectroscopic SRS imaging characterized by scanning a first pulsed beam across a stepwise reflective surface (such as a stepwise mirror or a reflective blazed grating) in a Littrow configuration to generate near continuous temporal delays relative to a second pulsed beam. Systems and methods also implement deep learning techniques for image restoration of spectroscopic SRS images using a trained encoder-decoder convolution neural network (CNN) which in some embodiments may be designed as a spatial-spectral residual net (SS-ResNet) characterized by two parallel filters including a first convolution filter on the spatial domain and a second convolution filter on the spectral domain.