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

14.6K
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...
14.6K
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
Introduction to Special Senses01:26

Introduction to Special Senses

7.6K
Sensory receptors play an integral part in comprehending our external and internal environments. They receive diverse stimuli, converting them into the nervous system's electrochemical signals. This conversion occurs as the stimulus alters the sensory neuron's cell membrane potential, instigating the generation of an action potential. This action potential is subsequently transmitted to the central nervous system (CNS), which integrates with other sensory data or higher cognitive...
7.6K
Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

643
Concrete exhibits specific behaviors under different compressive loads. Understanding this is crucial for understanding its structural integrity. When concrete undergoes uniaxial compression, it tends to develop cracks that run parallel to the direction of the force. These parallel cracks stem from localized tensile stresses that occur perpendicular to the compression direction. Additionally, angled cracks may appear due to the formation of shear planes.
As the concrete specimen fractures under...
643
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
Tactile and Chemical Senses01:27

Tactile and Chemical Senses

806
Tactile senses encompass touch, temperature, and pain, each mediated by specific receptors. Touch receptors detect mechanical energy or pressure against the skin. Sensory fibers from these receptors enter the spinal cord and relay information to the brain stem. Here, most fibers cross over to the opposite side of the brain. The touch information then moves to the thalamus, which projects a map of the body's surface onto the somatosensory areas of the parietal lobes in the cerebral cortex.
806

You might also read

Related Articles

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

Sort by
Same author

Design and evaluation of a long-wave infrared snapshot imaging spectrometer.

Optics express·2026
Same author

Mid-infrared computational spectroscopy scheme via metasurface filter arrays and physics-data dual-driven spectral reconstruction.

Optics express·2026
Same author

Impact of onset timing and operative duration on outcomes in chronic subdural hematoma: an exploratory retrospective study.

Frontiers in neurology·2026
Same author

Super-enhancer-associated lncRNA HDAC11-AS1 aggravates hepatocellular carcinoma progression by modulating HDAC11 and NUP210 expression via promoting super-enhancer activity.

Cellular oncology (Dordrecht, Netherlands)·2026
Same author

Design of a High Dynamic Range Acquisition System for Airborne VNIR Push-Broom Hyperspectral Camera.

Sensors (Basel, Switzerland)·2026
Same author

Design and On-Orbit Validation of a Compact Wide-Swath Spaceborne SWIR Push-Broom Camera.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Feb 6, 2026

Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging
10:01

Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging

Published on: September 8, 2017

8.2K

Infrared Image Super Resolution by Combining Compressive Sensing and Deep Learning.

Xudong Zhang1,2, Chunlai Li3, Qingpeng Meng4,5

  • 1University of Chinese Academy of Sciences, Beijing 101408, China. shzhangxd@aliyun.com.

Sensors (Basel, Switzerland)
|August 9, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new super-resolution method for infrared images, combining compressive sensing and deep learning. The approach effectively enhances image resolution, reducing noise and preserving high-frequency details for better visual quality.

Keywords:
compressive sensingconvolutional neural networksdeep learninginfrared imagessuper resolution

More Related Videos

Super-resolution Imaging of the Bacterial Division Machinery
08:47

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

12.2K
Super-Resolution Live Cell Imaging of Subcellular Structures
06:50

Super-Resolution Live Cell Imaging of Subcellular Structures

Published on: January 13, 2021

5.3K

Related Experiment Videos

Last Updated: Feb 6, 2026

Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging
10:01

Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging

Published on: September 8, 2017

8.2K
Super-resolution Imaging of the Bacterial Division Machinery
08:47

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

12.2K
Super-Resolution Live Cell Imaging of Subcellular Structures
06:50

Super-Resolution Live Cell Imaging of Subcellular Structures

Published on: January 13, 2021

5.3K

Area of Science:

  • Image processing
  • Computer vision
  • Infrared imaging

Background:

  • High-resolution infrared sensors are costly and difficult to implement.
  • Super-resolution techniques are needed to enhance the quality of low-resolution infrared images.

Purpose of the Study:

  • To develop a novel single image super-resolution method for infrared images.
  • To combine compressive sensing theory with deep learning for improved infrared image reconstruction.

Main Methods:

  • Utilizing compressive sensing to model low-resolution images as compressed samples of high-resolution ones.
  • Employing deep convolutional neural networks to address noise and reconstruct missing high-frequency information.
  • Concatenating compressive sensing and deep learning approaches for synergistic enhancement.

Main Results:

  • The proposed method outperforms existing techniques like SRCNN and ScSR in super-resolution tasks for infrared images.
  • Quantitative performance is validated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics.
  • Experimental results on open datasets and real-world infrared imaging demonstrate superior visual quality and detail preservation.

Conclusions:

  • The combined compressive sensing and deep learning method offers a powerful solution for infrared image super-resolution.
  • This approach effectively mitigates noise and recovers high-frequency details, leading to significantly improved image quality.
  • The method provides a practical and effective way to achieve high-resolution infrared imagery without expensive hardware.