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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

14.5K
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...
14.5K
Passive Filters01:27

Passive Filters

1.0K
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
1.0K
Active Filters01:25

Active Filters

1.3K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
1.3K
Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

6.8K
Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
6.8K
Structural Joints: Fibrous Joints01:03

Structural Joints: Fibrous Joints

3.7K
Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
Suture
All the bones of the skull, except for the mandible, are joined to each other by a fibrous joint called a suture. The fibrous connective tissue found at a suture strongly unites the adjacent skull bones and thus helps to protect the brain and form the face. In...
3.7K
Structural Joints: Cartilaginous Joints01:17

Structural Joints: Cartilaginous Joints

4.0K
As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
There are two types of cartilaginous joints:
Synchondrosis
A synchondrosis ("joined by cartilage") is a cartilaginous joint where bones are connected by hyaline cartilage. Synchondrosis may be temporary...
4.0K

You might also read

Related Articles

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

Sort by
Same author

Active interaction strategy generation for human-robot collaboration based on trust.

Visual computing for industry, biomedicine, and art·2025
Same author

Semisupervised Semantic Segmentation with Mutual Correction Learning.

Computational intelligence and neuroscience·2022
Same author

Galectin-1 enhances astrocytic BDNF production and improves functional outcome in rats following ischemia.

Neurochemical research·2010
Same author

Inclusion of telmisartan in mesocellular foam nanoparticles: drug loading and release property.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V·2010
Same author

The antiangiogenic efficacy of NGR-modified PEG-DSPE micelles containing paclitaxel (NGR-M-PTX) for the treatment of glioma in rats.

Journal of drug targeting·2010
Same author

Effects of 7-O substitutions on estrogenic and anti-estrogenic activities of daidzein analogues in MCF-7 breast cancer cells.

Journal of medicinal chemistry·2010

Related Experiment Video

Updated: Jan 28, 2026

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
14:02

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

6.2K

Depth image super-resolution reconstruction based on a modified joint trilateral filter.

Dongsheng Zhou1, Ruyi Wang1, Xin Yang2

  • 1Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian 116622, People's Republic of China.

Royal Society Open Science
|February 26, 2019
PubMed
Summary

This study introduces a novel depth image super-resolution (SR) method that enhances low-resolution images without external data. The technique preserves edge sharpness and suppresses noise, outperforming existing SR approaches.

Keywords:
depth imagejoint trilateral filtersparse codesuper-resolution

More Related Videos

Ground State Depletion Super-resolution Imaging in Mammalian Cells
07:55

Ground State Depletion Super-resolution Imaging in Mammalian Cells

Published on: November 5, 2017

7.5K
Super-resolution Imaging of Neuronal Dense-core Vesicles
09:30

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

10.1K

Related Experiment Videos

Last Updated: Jan 28, 2026

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
14:02

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

6.2K
Ground State Depletion Super-resolution Imaging in Mammalian Cells
07:55

Ground State Depletion Super-resolution Imaging in Mammalian Cells

Published on: November 5, 2017

7.5K
Super-resolution Imaging of Neuronal Dense-core Vesicles
09:30

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

10.1K

Area of Science:

  • Computer Vision
  • Signal Processing
  • Image Reconstruction

Background:

  • Depth image super-resolution (SR) typically requires external databases or high-resolution (HR) images for prior information.
  • Existing methods face limitations due to their dependence on external data sources.

Purpose of the Study:

  • To propose a novel reference-free depth image SR method.
  • To enhance low-resolution (LR) depth images by preserving detailed information and suppressing noise.
  • To overcome the limitations of database-dependent SR techniques.

Main Methods:

  • A high-quality edge map is constructed using sparse coding with a dictionary learned from multi-scale original images.
  • A modified joint trilateral filter, guided by the edge map, is employed for depth image interpolation.
  • Gradient and structural similarity (SSIM) information are incorporated during interpolation to preserve details and reduce noise.

Main Results:

  • The proposed method successfully preserves the sharpness of image edges.
  • It effectively suppresses noise while enhancing image resolution.
  • Experimental results demonstrate superiority over state-of-the-art depth image SR methods.

Conclusions:

  • The developed SR method achieves high-quality depth image enhancement without relying on external databases.
  • It offers a robust solution for depth image SR, maintaining structural integrity and detail.
  • The approach represents a significant advancement in reference-free image super-resolution techniques.