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Infrared and visible image fusion algorithm based on a cross-layer densely connected convolutional network.

Ruixing Yu, Weiyu Chen, Bing Zhu

|April 26, 2022

Summary

This summary is machine-generated.

This study introduces a new infrared and visible image fusion method using a densely connected convolutional network. The approach effectively preserves target saliency and image textures, outperforming existing techniques.

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Infrared and visible image fusion is crucial for enhancing image quality and information extraction.
Existing methods often struggle to preserve both target saliency in infrared images and texture details in visible images simultaneously.

Purpose of the Study:

To propose a novel infrared and visible image fusion method that preserves target saliency and image textures.
To minimize the omission of effective information from source images during the fusion process.

Main Methods:

A densely connected convolutional network comprising an encoder, fusion, and decoder was designed.
A loss function based on a variational model was developed to retain thermal radiation information and image details.

Main Results:

The proposed method demonstrated superior performance compared to state-of-the-art techniques across six evaluation metrics.
The fusion results showed enhanced preservation of clear targets and textures from both infrared and visible images.

Conclusions:

The novel fusion method effectively preserves critical information from both infrared and visible images.
The densely connected convolutional network and variational loss function contribute to improved fusion quality.