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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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A comprehensive review of deep learning-based single image super-resolution.

Syed Muhammad Arsalan Bashir^1,2, Yi Wang¹, Mahrukh Khan³

¹School of Electronics and Information, Northwestern Polytechnical University, Xi'an, Shaanxi, China.

Peerj. Computer Science

|July 29, 2021

Summary

This survey details advancements in image super-resolution (SR), focusing on deep learning methods alongside classical techniques. It categorizes SR approaches and highlights key deep learning models for enhanced image resolution.

Keywords:

Artificial intelligence Convolutional neural networks (CNN)Deep learning Generative adversarial networks (GAN)Image super-resolution Neural networks Single-image super-resolution (SISR)Super-resolution

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Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Image super-resolution (SR) is crucial for enhancing image detail in computer vision.
Significant progress in SR over two decades, particularly with deep learning.
Classical SR methods laid the groundwork for modern techniques.

Purpose of the Study:

To provide a comprehensive survey of recent single-image super-resolution (SR) progress.
To detail deep learning-based SR methods and compare them with classical approaches.
To categorize SR methods and discuss challenges, metrics, and datasets.

Main Methods:

Classification of SR methods into classical, supervised learning, unsupervised learning, and domain-specific.
Review and evaluation of state-of-the-art deep learning SR models.
Introduction to SR problem, image quality metrics, datasets, and challenges.

Main Results:

Deep learning methods have significantly advanced image super-resolution capabilities.
Categorization provides a structured overview of diverse SR techniques.
Evaluation highlights the performance of leading SR models like EDSR, CinCGAN, MSRN, Meta-RDN, RBPN, SAN, SRFBN, and WRAN.

Conclusions:

Deep learning represents the forefront of image super-resolution research.
Future research should address open problems and explore emerging trends in SR.
This survey offers a valuable resource for researchers in the field of image super-resolution.