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Related Experiment Videos

Visible-Infrared Fusion Based on CNN and Deformable Transformer.

Xiaoyi Wang1,2, Xiansong Gu1, Bin Li2

  • 1College of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Journal of Imaging
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

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Deconvolution01:20

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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...

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This study introduces a novel infrared-visible image object detection method using Convolutional Neural Networks (CNNs) and Deformable Transformers. The approach enhances object detection in challenging conditions like low light and occlusion.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Traditional feature extraction and multi-modal fusion methods have limitations.
  • Integrating infrared and visible imagery presents challenges in fusing local and global information.

Purpose of the Study:

  • To propose an advanced object detection architecture for infrared-visible images.
  • To improve multi-modal information fusion and feature extraction for enhanced detection accuracy.

Main Methods:

  • Integration of Convolutional Neural Networks (CNNs) for local feature modeling.
  • Utilization of Deformable Transformers for global contextual information capture.
  • Implementation of a detection-aware multi-task optimization mechanism.
Keywords:
deformable transformerinfrared and visible image fusionobject detection

Related Experiment Videos

Main Results:

  • Achieved competitive or superior performance on M3FD and LLVIP datasets.
  • Obtained the highest mAP50 scores: 74.2% on M3FD and 98.6% on LLVIP.
  • Demonstrated significant improvements over existing methods, including PIAFusion.

Conclusions:

  • The proposed CNN-Transformer architecture effectively fuses multi-modal image data.
  • The method shows practical effectiveness and robustness in complex environments with low light and occlusion.