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DCFNet: Infrared and Visible Image Fusion Network Based on Discrete Wavelet Transform and Convolutional Neural

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  • 1School of Electronic Engineering, Xi'an Shiyou University, Xi'an 710312, China.

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|July 13, 2024
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Summary
This summary is machine-generated.

This study introduces a new infrared and visible-light image fusion algorithm using discrete wavelet transform (DWT) and convolutional neural networks (CNNs). The method enhances detail and target visibility in fused images, outperforming existing techniques.

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convolutional neural networkdiscrete wavelet transformimage fusioninfrared and visible images

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Current image fusion algorithms struggle with missing details, blurred target information, and poor visual quality.
  • Infrared and visible-light image fusion is crucial for applications requiring comprehensive scene understanding.

Purpose of the Study:

  • To develop an advanced infrared and visible-light image fusion algorithm addressing limitations of existing methods.
  • To improve the clarity of target information and overall visual quality in fused images.

Main Methods:

  • Proposed algorithm integrates discrete wavelet transform (DWT) and convolutional neural networks (CNNs) within an autoencoder backbone.
  • DWT and inverse DWT (IDWT) layers optimize frequency-domain feature extraction and reconstruction.
  • Incorporated bottleneck residual blocks and a coordinate attention mechanism to enhance feature characterization.
  • Employed an l1-norm fusion strategy and a weighted loss function (pixel, gradient, structural loss) for network optimization.

Main Results:

  • The proposed algorithm effectively fuses infrared and visible-light images, producing clearer results with enhanced scene information.
  • Experimental evaluations on public datasets demonstrate superior performance in both subjective and objective metrics.
  • Generalization experiments confirm the network's robust ability to adapt to diverse image data.

Conclusions:

  • The developed DWT-CNN based fusion algorithm significantly improves image fusion quality by preserving detailed information and enhancing target visibility.
  • The method offers a visually natural and harmonious fused image, validating its effectiveness and generalization capability.