Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Restorative Care01:19

Restorative Care

2.4K
Restorative care is provided once a patient has been discharged from a healthcare facility and requires additional services. The additional services include home care, rehabilitation programs, and extended care. Restorative care centers help the patient regain their previous level of functioning or acquire a new level of functioning due to the incapacitating effects of a disease or a disability. It aims to assist patients in enhancing their quality of life by encouraging independence,...
2.4K
The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

56.1K
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
56.1K
Absolute and Local Extreme Values01:22

Absolute and Local Extreme Values

84
The highest and lowest values of a function, relative to a reference axis, are known as extreme values. These include absolute maximum and absolute minimum values, which represent the highest and lowest points the function reaches across its entire domain. Within a restricted portion of the function, the highest and lowest values are referred to as local maximum and local minimum values, respectively.Periodic functions, such as sine and cosine, show extreme values at infinitely many points due...
84
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.5K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.5K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.0K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
7.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparison of deep learning approaches for extreme low-SNR image restoration.

GigaScience·2026
Same author

Genetic or pharmacological disruption of the MSH3 Y245/K246 IDL binding pocket slows CAG repeat expansion.

NAR molecular medicine·2026
Same author

Correlative scanning electron and super-resolution structured illumination microscopy.

bioRxiv : the preprint server for biology·2026
Same author

Journal Club Article: The STEROHCA trial - Optimizing post resuscitation haemodynamics by prehospital high dose corticosteroids.

Resuscitation plus·2025
Same author

Low-Cost Spinning Disk Confocal Microscopy with a 25-Megapixel Camera.

Sensors (Basel, Switzerland)·2025
Same author

Spinning disk confocal microscopy with a 25 Megapixel Camera.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Feb 7, 2026

Point-Of-Care Ultrasound Screening for Proximal Lower Extremity Deep Venous Thrombosis
06:45

Point-Of-Care Ultrasound Screening for Proximal Lower Extremity Deep Venous Thrombosis

Published on: February 10, 2023

16.3K

Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration.

Nasreen Elizabeth Buhn1, Sriya Reddy Adunur2, Joseph Hamilton3

  • 1Biological Sciences Department, California Polytechnic State University, San Luis Obispo, California, 93407.

Biorxiv : the Preprint Server for Biology
|February 6, 2026
PubMed
Summary

A new fluorescence microscopy dataset and image stitching method address challenges in deep learning-based image denoising. This enables better evaluation of denoising models for low signal-to-noise ratio (SNR) images, improving live-cell imaging analysis.

Keywords:
Deep LearningDenoisingFluorescence MicroscopyImage RestorationImage StitchingPhototoxicity

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.5K

Related Experiment Videos

Last Updated: Feb 7, 2026

Point-Of-Care Ultrasound Screening for Proximal Lower Extremity Deep Venous Thrombosis
06:45

Point-Of-Care Ultrasound Screening for Proximal Lower Extremity Deep Venous Thrombosis

Published on: February 10, 2023

16.3K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.5K

Area of Science:

  • Microscopy
  • Computational Biology
  • Image Analysis

Background:

  • Live-cell fluorescence microscopy is crucial for studying dynamic cellular processes.
  • Phototoxicity and photobleaching from microscopy reduce image quality (low signal-to-noise ratio, SNR) and can harm cells.
  • Deep learning (DL) can restore low-SNR images but requires large datasets and significant GPU memory for large images.

Purpose of the Study:

  • To introduce a comprehensive fluorescence microscopy dataset for evaluating DL denoising methods.
  • To present an image stitching technique to overcome GPU memory limitations for large image processing.
  • To benchmark state-of-the-art DL denoising models using the new dataset.

Main Methods:

  • A diverse dataset of 324 paired high/low-SNR fluorescence microscopy images (4-282 megapixels) was created, varying specimen, staining, and imaging parameters.
  • Three DL denoising models (transformer-based, CNN, unsupervised) were evaluated.
  • An image stitching method was developed to process large images in manageable crops.

Main Results:

  • The new dataset offers a varied benchmark for assessing DL denoising performance across different imaging conditions.
  • The image stitching method effectively addresses GPU memory constraints for processing large microscopy images.
  • The supervised transformer-based DL model demonstrated the highest denoising performance.

Conclusions:

  • The developed dataset and stitching method facilitate robust evaluation of DL denoising techniques for fluorescence microscopy.
  • The supervised transformer-based model shows superior denoising capabilities, though with increased training time.
  • These advancements support improved analysis of dynamic cellular processes using low-light microscopy.