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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: Feb 22, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Focus-guided feature fusion network for lightweight image super-resolution.

Haoran Jia¹, Xin Wang¹, Tongtai Cao¹

¹Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.

Neural Networks : the Official Journal of the International Neural Network Society

|February 20, 2026

Summary

This study introduces FocusSRNet, a novel approach for single image super-resolution (SISR). By incorporating image focus information, it achieves high-accuracy reconstruction with a lightweight network design.

Keywords:

Asymmetric convolution Focus measurement Image super-resolution

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Last Updated: Feb 22, 2026

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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:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Single Image Super-Resolution (SISR) faces a trade-off between model complexity and reconstruction accuracy.
Current lightweight networks often neglect valuable latent information in low-resolution (LR) images.

Purpose of the Study:

To propose a novel Focus-Guided Feature Fusion Network (FocusSRNet) for SISR.
To enhance feature representation sufficiency and discriminability by integrating image focus information as a prior signal.

Main Methods:

Developed FocusSRNet with three core modules: Focus Measurement Unit (FMU), Large Receptive Field Feature Extraction Module (LRFFEM), and Asymmetric Channel Feature Enhancement Module (ACFEM).
Introduced image focus information as a novel guidance signal in SISR tasks.

Main Results:

FocusSRNet demonstrated superior performance compared to existing state-of-the-art methods on multiple benchmark datasets.
The focus-guided mechanism significantly improved reconstruction quality.

Conclusions:

The proposed focus-guided mechanism offers a new perspective for lightweight and high-accuracy super-resolution reconstruction.
FocusSRNet provides a feasible approach for enhancing SISR performance by leveraging image focus priors.