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

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Published on: December 15, 2023

Comprehensive Review of Deep Learning Approaches for Single-Image Super-Resolution.

Zirun Liu^1,2, Shijie Jiang³, Shuhan Feng³

¹Longmen Laboratory, Luoyang 471000, China.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

Deep learning-based single-image super-resolution (SISR) enhances image resolution by overcoming system limitations. This review provides a framework for understanding SISR

Keywords:

deep learning image quality assessment single-image super-resolution

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Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Single-image super-resolution (SISR) addresses physical limitations in imaging systems to improve resolution.
Deep learning methods have become central to advancing SISR techniques.

Purpose of the Study:

To systematically introduce deep learning-based SISR methods.
To propose a method-oriented classification framework for SISR.
To explore theoretical basis, technological evolution, and domain-specific applications of SISR.

Main Methods:

A method-oriented classification framework is proposed.
Key technical components like datasets, upsampling strategies, objective functions, and quality assessment are analyzed.
Classic SISR model reconstruction results are compared.

Main Results:

A systematic knowledge framework for SISR is presented.
In-depth analysis of benchmark datasets, multi-scale upsampling, and objective function optimization.
Comparative evaluation of established SISR models.

Conclusions:

The review provides a comprehensive overview of deep learning-based SISR.
Identifies limitations and proposes future research directions for SISR.
Offers significant reference value for the advancement of SISR technology.