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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: Aug 4, 2025

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Published on: December 1, 2016

Pixel-Perfect Structure-From-Motion With Featuremetric Refinement.

Paul-Edouard Sarlin, Philipp Lindenberger, Viktor Larsson

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 6, 2023

Summary

This summary is machine-generated.

This study refines 3D reconstruction by directly aligning image features across multiple views. It improves keypoint localization and camera pose accuracy, enhancing sparse 3D reconstruction for large datasets.

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Last Updated: Aug 4, 2025

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

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Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy

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Published on: March 6, 2018

Area of Science:

Computer Vision
3D Reconstruction
Geometric Modeling

Background:

Sparse 3D reconstruction relies on repeatable local features.
Classical methods detect keypoints once, leading to localization errors and geometric inaccuracies.

Purpose of the Study:

To refine keypoint localization and camera pose estimation in structure-from-motion.
To improve the accuracy and robustness of 3D reconstruction.

Main Methods:

Direct alignment of low-level image information across multiple views.
Two-stage refinement: initial keypoint adjustment and post-processing refinement.
Optimization of a featuremetric error using dense features from a neural network.

Main Results:

Significantly improved accuracy of camera poses and scene geometry.
Robustness to large detection noise and appearance changes.
Enhanced performance across various keypoint detectors and challenging viewing conditions.

Conclusions:

The proposed refinement method enhances sparse 3D reconstruction accuracy.
Enables pixel-perfect crowd-sourced localization at scale for large image collections.