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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 developed.

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Related Experiment Video

Updated: Jun 3, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Multiframe image super-resolution adapted with local spatial information.

Liangpei Zhang¹, Qiangqiang Yuan, Huanfeng Shen

¹The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China.

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|March 9, 2011

Summary

This summary is machine-generated.

This study introduces a block-based approach for super-resolution image reconstruction, adapting prior models and regularization parameters locally. This method improves performance by addressing spatially adaptive properties and reducing computational load.

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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

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Last Updated: Jun 3, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

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

Area of Science:

Computer Vision
Image Processing

Background:

Super-resolution image reconstruction aims to enhance image resolution from low-resolution observations.
Maximum a posteriori (MAP) models are common but often use fixed priors and regularization parameters.
Existing methods neglect local image properties and face high computational costs.

Purpose of the Study:

To develop a block-based, local spatially adaptive reconstruction algorithm.
To reduce computational load in super-resolution image reconstruction.
To improve reconstruction performance by adapting priors and regularization parameters.

Main Methods:

The proposed method divides the target image into blocks.
Structure tensor analysis is employed to determine local spatial properties of each block.
Adaptive prior models and regularization parameters are applied based on block properties.

Main Results:

The block-based method demonstrates superior performance compared to fixed-parameter approaches.
Local spatial adaptivity leads to improved super-resolution image reconstruction.
Reduced computational load is achieved through the block-based processing.

Conclusions:

The proposed block-based local spatially adaptive algorithm effectively enhances super-resolution image reconstruction.
Adapting priors and regularization parameters locally significantly improves results.
This approach offers a more efficient and effective solution for super-resolution tasks.