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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...

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Related Experiment Video

Updated: Jun 16, 2026

Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging
09:46

Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

Published on: April 28, 2022

Self-Supervised Denoising Network for Quantitative Stimulated Raman Scattering Microscopy in Cross-Type Samples.

Yunfan Jiang1, Yuan Xue1, Chen Chen1

  • 1National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering); School of Biological Science and Medical Engineering, and School of Engineering Medicine, Beihang University, Beijing 100191, China.

Analytical Chemistry
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

DenoiseGAN, a novel self-supervised network, effectively reduces noise in Stimulated Raman Scattering (SRS) microscopy images. This advancement enhances high-sensitivity imaging and preserves crucial molecular data for biological and medical applications.

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Implementation of a Nonlinear Microscope Based on Stimulated Raman Scattering
09:13

Implementation of a Nonlinear Microscope Based on Stimulated Raman Scattering

Published on: July 6, 2019

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Last Updated: Jun 16, 2026

Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging
09:46

Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

Published on: April 28, 2022

Implementation of a Nonlinear Microscope Based on Stimulated Raman Scattering
09:13

Implementation of a Nonlinear Microscope Based on Stimulated Raman Scattering

Published on: July 6, 2019

Area of Science:

  • Biomedical Imaging
  • Microscopy Techniques
  • Artificial Intelligence in Science

Background:

  • Stimulated Raman Scattering (SRS) microscopy is crucial for biology and medicine.
  • Image noise in SRS limits sensitivity and speed.
  • Developing effective denoising methods is essential for advancing SRS applications.

Purpose of the Study:

  • Introduce DenoiseGAN, a self-supervised network for noise reduction in SRS images.
  • Evaluate DenoiseGAN's performance across diverse biological samples.
  • Demonstrate DenoiseGAN's capability to preserve quantitative molecular information.

Main Methods:

  • Developed a self-supervised deep learning network, DenoiseGAN.
  • Applied DenoiseGAN to SRS images of gastric cancer cells, bladder cancer tissues, and fungal cells.
  • Quantitatively assessed image quality using signal-to-noise ratio and structural similarity index.

Main Results:

  • Achieved up to a 7-fold increase in signal-to-noise ratio.
  • Maintained a structural similarity index measure greater than 0.81.
  • Successfully preserved quantitative molecular information (protein/lipid ratio, D2O metabolism) for diagnostic and testing purposes.

Conclusions:

  • DenoiseGAN significantly improves SRS image quality by reducing noise while preserving structural integrity.
  • The network enables high-sensitivity and high-speed SRS imaging.
  • DenoiseGAN shows broad applicability for denoising SRS images in various biological and medical contexts.