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Related Experiment Video

Updated: Apr 15, 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

MBFTFuse: A Triple-Path Adversarial Network Based on Modality Balancing and Feature-Tracing Compensation for Infrared

Mingxi Chen¹, Bingting Zha¹, Rui Yang¹

¹School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210018, China.

Sensors (Basel, Switzerland)

|April 14, 2026

Summary

MBFTFuse, an adversarial fusion network, enhances infrared and visible image fusion by balancing modalities and tracing features. This method effectively highlights targets while preserving textures, improving computer vision technologies.

Keywords:

adversarial learning feature tracing image fusion modality bias

Related Experiment Videos

Last Updated: Apr 15, 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
Image Processing
Artificial Intelligence

Background:

Infrared and visible image fusion integrates data from different sensors.
Existing methods suffer from modality bias, weakening targets or losing texture details.

Purpose of the Study:

To propose MBFTFuse, an adversarial fusion network for improved infrared and visible image fusion.
To address modality bias and information loss in existing fusion techniques.

Main Methods:

MBFTFuse utilizes a triple-path generator (modality-balancing and feature-tracing paths) and dual discriminators.
Key components include a multi-cognitive modality-balancing module and a Feature-Tracing Attention Module.
Pixel loss based on intensity histograms optimizes inter-modal balance.

Main Results:

MBFTFuse effectively highlights infrared targets while preserving visible textures.
Demonstrated superior performance against nine state-of-the-art methods across three datasets.
Improved downstream object detection tasks compared to existing methods.

Conclusions:

MBFTFuse overcomes modality bias in image fusion.
The method enhances target visibility and texture preservation.
Contributes to advancing sensor-driven computer vision.