Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Deconvolution

Deconvolution

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...

You might also read

Related Articles

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

Sort by

Same author

Dual-functional synergistic modification of cellulose with phosphate and amidoxime for high-efficiency uranium capture in acidic wastewater.

Carbohydrate polymers·2026

Same author

Theoretical Study on the Formation Mechanism of Ti(C,N) Inclusions and Titanium Content Control in High-Grade Non-Oriented Silicon Steel.

Materials (Basel, Switzerland)·2026

Same author

Anti-influenza virus activities of triterpenes from the antler-shaped fruiting bodies of Ganoderma lucidum.

Phytochemistry·2026

Same author

Clerodane diterpenes lactones, alkaloids, and furan derivatives with hepatoprotective activity isolated from the tuberous root of Paratinospora sagittata (Oliv.) Wei Wang.

Phytochemistry·2026

Same author

Differential Effects of Saturated and Unsaturated Fatty Acids on Colorectal Cancer via IDO1 Signaling.

Cancer medicine·2026

Same author

Nine feruloyltyramine derivatives and one pyrrole with hepatoprotective activity isolated from fresh Portulaca oleracea L.

Fitoterapia·2026

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 31, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A Fully Convolutional Network-Based Tube Contour Detection Method Using Multi-Exposure Images.

Xiaoqi Cheng¹, Junhua Sun¹, Fuqiang Zhou¹

¹School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This summary is machine-generated.

A novel fully convolutional network (FCN) method enhances tube contour detection in complex backgrounds using multi-exposure (ME) images. This approach improves contour accuracy and integrity for optical 3D reconstruction tasks.

Keywords:

U-Net dilation operation fully convolutional network multi-exposure images tube contour detection

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Oct 31, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Computer Vision
Image Processing
3D Reconstruction

Background:

Accurate tube contour detection is crucial for 3D reconstruction.
Complex backgrounds pose challenges for traditional contour detection methods.

Purpose of the Study:

To propose a robust tube contour detection method for complex backgrounds.
To improve the accuracy and integrity of detected tube contours.

Main Methods:

Utilized a fully convolutional network (FCN) with a U-Net architecture.
Employed multi-exposure (ME) images as input for enhanced dynamic range information.
Introduced a novel loss function to mitigate label inaccuracies.
Developed a new dataset (METCD) and evaluation metric (DIA-ODS).

Main Results:

The proposed FCN-based method significantly improved tube contour detection.
Enhanced accuracy and integrity of contours were observed in complex scenes.
The new dataset and metric facilitated comprehensive method evaluation.

Conclusions:

The FCN-based method with ME images offers an effective solution for challenging tube contour detection.
The proposed loss function and evaluation metrics contribute to more reliable results.
This work advances the capabilities of optical 3D reconstruction from 2D images.