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Super-resolution Fluorescence Microscopy

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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: Sep 28, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Disparity-constrained stereo endoscopic image super-resolution.

Tianyi Zhang^1,2, Yun Gu^1,2, Xiaolin Huang^1,2

¹Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.

International Journal of Computer Assisted Radiology and Surgery

|April 4, 2022

Summary

This summary is machine-generated.

This study introduces a disparity-constrained stereo super-resolution network (DCSSRnet) to enhance low-resolution stereo endoscopic images. The novel approach effectively improves image quality for better 3D surgical context in minimally invasive procedures.

Keywords:

Disparity constraint Endoscopic surgery Stereo image Super-resolution

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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

Area of Science:

Medical Imaging
Computer Vision
Surgical Technology

Background:

Stereo cameras enhance 3D visualization in computer-assisted minimally invasive surgery.
Limited lens and sensor size at endoscope tips result in low-resolution stereo endoscopic images.
Effective utilization of stereo information for super-resolution (SR) in endoscopic imaging is challenging.

Purpose of the Study:

To develop a novel stereo super-resolution network for enhancing low-resolution stereo endoscopic images.
To address the challenge of effectively exploiting stereo information for super-resolution in confined endoscopic environments.

Main Methods:

Proposed a disparity-constrained stereo super-resolution network (DCSSRnet).
Incorporated a disparity constraint mechanism within a deep neural network framework.
Utilized effective feature extractors and atrous parallax attention modules.

Main Results:

DCSSRnet demonstrated superior performance on the da Vinci and Medtronic datasets compared to existing SR methods.
Quantitative measurements confirmed significant improvements in image reconstruction.
Ablation studies validated the effectiveness of individual components within the DCSSRnet framework.

Conclusions:

DCSSRnet offers a promising solution for enhancing the spatial resolution of stereo endoscopic image pairs.
Disparity consistency in stereo images provides effective supervision for image reconstruction.
The model can improve the quality of stereo endoscopic images, aiding surgical systems.