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

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

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

Transforaminal Endoscopic Extradural Onlay Patch Graft Repair of Spontaneous Spinal Type 1a Cerebrospinal Fluid Leaks: A Case Series.

Operative neurosurgery (Hagerstown, Md.)·2026

Same author

Single-cell transcriptomics reveals MAFB-driven macrophage reprogramming and immune divergence in recurrent glioblastoma.

BMC cancer·2026

Same author

Iron plaque drives mangrove arsenic detoxification and root adaptation in coastal wetland Avicennia marina seedlings.

Marine environmental research·2026

Same author

Luteolin-Loaded WPI/Pueraria lobata Amylopectin Composite Gel Improves Adipocyte Thermogenesis and Insulin Sensitivity.

ACS applied bio materials·2026

Same author

A MXene@GQDs hybrid fluorescent array for rapid identification and quantification of foodborne pathogens.

Mikrochimica acta·2026

Same author

Nobiletin ameliorates rheumatoid arthritis inflammation via suppression of the IL-17/NF-κB/MMP9 pathway.

International immunopharmacology·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: May 6, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Self-Supervised Contrastive Learning and GAN-Based Denoising for High-Fidelity HumanNeRF Images.

Qian Xu¹, Wenxuan Xu¹, Meng Huang¹

¹School of Computer and Control Engineering, Yan Tai University, Yantai 264005, China.

Sensors (Basel, Switzerland)

|January 10, 2026

Summary

This summary is machine-generated.

This study introduces a novel image denoising method combining self-supervised contrastive learning and Generative Adversarial Networks (GANs) to improve HumanNeRF image quality. The approach effectively removes noise while preserving crucial human details for better 3D reconstruction.

Keywords:

HumanNeRF contrastive learning generative adversarial networks image denoising self-supervised learning

Related Experiment Videos

Last Updated: May 6, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Vision
Artificial Intelligence

Background:

HumanNeRF generates realistic 3D human models but suffers from image noise and detail loss.
This degradation stems from incomplete training data and rendering process sampling noise.

Purpose of the Study:

To develop an effective image denoising method for HumanNeRF-generated images.
To enhance detail fidelity and overall image realism.

Main Methods:

Utilized self-supervised contrastive learning to differentiate noise from human details without external labels.
Employed Generative Adversarial Networks (GANs) for adversarial training to refine image realism and detail representation.

Main Results:

Successfully removed noise from HumanNeRF images.
Significantly improved detail fidelity and image quality.
Demonstrated superior performance in enhancing human image realism.

Conclusions:

The proposed method effectively denoises HumanNeRF images.
It enhances detail fidelity, supporting improved 3D human reconstruction and rendering.
Combines self-supervised learning and GANs for robust image enhancement.