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Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

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Related Experiment Video

Updated: Jun 29, 2025

Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

Published on: January 14, 2020

HoloSR: deep learning-based super-resolution for real-time high-resolution computer-generated holograms.

Siwoo Lee, Seung-Woo Nam, Juhyun Lee

|April 4, 2024

Summary

This summary is machine-generated.

HoloSR enhances 3D imaging by generating high-resolution holograms from low-resolution RGBD images using deep learning. This novel approach enables real-time, realistic 3D image production without interpolation.

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Last Updated: Jun 29, 2025

Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

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Published on: January 14, 2020

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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Published on: February 8, 2014

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Published on: September 8, 2017

Area of Science:

Computer Vision
Holography
Deep Learning

Background:

Generating high-resolution computer-generated holograms (CGH) from low-resolution data is challenging.
Real-time 3D image reconstruction requires efficient super-resolution techniques.

Purpose of the Study:

To introduce HoloSR, a deep learning-based super-resolution method for CGH.
To enable the direct generation of high-resolution CGH from low-resolution RGBD images.

Main Methods:

HoloSR integrates an enhanced deep super-resolution network with resize and convolution layers.
The method directly generates high-resolution CGH without interpolation.
Evaluated upscaling scales up to ×4 using quantitative metrics (SSIM, PSNR).

Main Results:

HoloSR successfully achieved super-resolution for CGH generation.
Demonstrated effective generation of high-resolution holograms from low-resolution RGBD inputs.
Validated through both simulation and experimental results.

Conclusions:

HoloSR provides a novel and effective approach for super-resolution in CGH.
The method facilitates real-time production of realistic 3D images.
Applicable to both supervised and unsupervised learning scenarios.