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

Reflection of Waves01:07

Reflection of Waves

3.9K
When a wave travels from one medium to another, it gets reflected at the boundary of the second medium. A common example of this is when a person yells at a distance from a cliff and hears the echo of their voice. The sound waves (longitudinal waves) traveling in the air are reflected from the bounding cliff. Similarly, flipping one end of a string whose other end is tied to a wall causes a pulse (transverse wave) to travel through the string, which gets reflected upon reaching the wall. In...
3.9K
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

141
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
141
Clipper Circuit01:18

Clipper Circuit

534
A clipper circuit is a fundamental wave-shaping device that harnesses the unique properties of diodes to alter and control waveform characteristics. This technology is widely used in electronic devices, especially in television and radar communication systems, where it enhances waveform modulation in both transmitters and receivers.
The operation of a clipper circuit can be exemplified by analyzing a dual-clipper configuration setup that integrates two ideal diodes, each paired with a biasing...
534
Propagation of Waves01:07

Propagation of Waves

2.4K
When a wave propagates from one medium to another, part of it may get reflected in the first medium, and part of it may get transmitted to the second medium. In such a case, the interface of the two mediums can be considered as a boundary that is neither fixed nor free.
Consider a scenario where a wave propagates from a string of low linear mass density to a string of high linear mass density. In such a case, the reflected wave is out of phase with respect to the incident wave, however the...
2.4K
Termination of Translation01:44

Termination of Translation

5.6K
5.6K
Deconvolution01:20

Deconvolution

233
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
233

You might also read

Related Articles

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

Sort by
Same author

Progressive Structure Preservation and Detail Refinement for Remote Sensing Single-Image Super-Resolution.

IEEE transactions on neural networks and learning systems·2025
Same author

Improving object detection in optical devices using a multi-hierarchical cyclable structure-aware rain removal network.

Optics express·2024
Same author

Wavelet Approximation-Aware Residual Network for Single Image Deraining.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Wavelet Pyramid Recurrent Structure-Preserving Attention Network for Single Image Super-Resolution.

IEEE transactions on neural networks and learning systems·2023
Same author

EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2023
Same author

Structure-transferring edge-enhanced grid dehazing network.

Optics express·2023

Related Experiment Video

Updated: Aug 25, 2025

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.7K

Translation-invariant context-retentive wavelet reflection removal network.

Wei-Yen Hsu, Wan-Jia Wu

    Optics Express
    |October 15, 2022
    PubMed
    Summary

    This study introduces a new deep learning network for removing reflections from images. The novel approach effectively removes reflections while preserving image background context, outperforming existing methods.

    Area of Science:

    • Computer Vision
    • Deep Learning
    • Image Processing

    Background:

    • Removing reflections from images is a challenging problem in computer vision.
    • Existing deep learning methods often fail in specific situations or lack robust validation.
    • Limitations in current methods hinder effective real-world reflection elimination.

    Purpose of the Study:

    • To propose a novel network for effective reflection removal from images.
    • To address the limitations of existing methods in handling diverse reflection scenarios.
    • To develop a method that retains background context while removing reflections.

    Main Methods:

    • A Translation-invariant Context-retentive Wavelet Reflection Removal Network is proposed.
    • Wavelet transform is used to decompose images into sub-images.

    More Related Videos

    Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
    06:04

    Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

    Published on: January 17, 2025

    694
    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
    09:43

    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

    Published on: March 20, 2017

    10.0K

    Related Experiment Videos

    Last Updated: Aug 25, 2025

    Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
    11:00

    Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

    Published on: July 19, 2016

    11.7K
    Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
    06:04

    Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

    Published on: January 17, 2025

    694
    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
    09:43

    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

    Published on: March 20, 2017

    10.0K
  • Context Retention Subnetwork (CRSn) and Detail-enhanced Reflection layer removal Subnetwork are utilized.
  • Novel context level blending and inverse wavelet transform are introduced for low-frequency components.
  • Detail-enhanced Reflection Information Transmission aids in separating reflection and transmission layers.

    • Main Results:

    • The proposed network effectively removes reflections from both low-frequency and high-frequency image components.
    • Background context is successfully retained during the reflection removal process.
    • Quantitative and visual experimental results show superior performance compared to state-of-the-art methods on benchmark datasets.

    Conclusions:

    • The novel network provides a significant advancement in reflection removal technology.
    • The method demonstrates robustness across various reflection scenarios.
    • It offers a more effective solution for restoring clean images compared to existing approaches.