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Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion.

Chan-Gi Im¹, Dong-Min Son¹, Hyuk-Ju Kwon¹

¹School of Electronic and Electrical Engineering, Kyungpook National University, 80 Deahakro, Buk-Gu, Daegu 41566, Korea.

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Summary

This study introduces a fast image fusion method using DenseFuse, a convolutional neural network (CNN), to accelerate visible and near-infrared (NIR) image synthesis. The new CNN-based approach significantly improves processing speed while maintaining high image quality.

Keywords:

deep learning image fusion infrared image supervised learning visible image

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Rule-based visible and near-infrared (NIR) image synthesis methods suffer from slow processing speeds.
Existing learning-based methods often compromise image quality or visibility.

Purpose of the Study:

To develop a fast and effective image fusion method for synthesizing visible and NIR images.
To improve upon the processing speed of traditional rule-based methods using convolutional neural networks (CNNs).

Main Methods:

A novel image fusion method utilizing DenseFuse, a CNN-based architecture, is proposed.
The method incorporates a raster scan algorithm for dataset preparation and a classification technique based on luminance and variance.
Feature map synthesis within a specific fusion layer is investigated and compared to other fusion layer approaches.

Main Results:

The proposed DenseFuse method achieves superior image quality and visibility compared to existing learning-based methods.
The synthesized images retain the high visual quality characteristic of rule-based methods.
Processing time is reduced by at least three times compared to the rule-based image synthesis method.

Conclusions:

The DenseFuse-based image fusion method offers a significant improvement in processing speed for visible and NIR image synthesis.
This approach successfully balances enhanced speed with high-quality image synthesis, outperforming existing learning-based techniques.