Infrared and Visible Image Fusion using a Deep Learning Framework

TL;DR

This paper introduces a deep learning-based image fusion method that combines infrared and visible images into a single, feature-rich image, demonstrating superior performance over existing techniques.

Contribution

The paper presents a novel deep learning framework for image fusion that effectively combines multi-layer features from infrared and visible images, achieving state-of-the-art results.

Findings

Achieves state-of-the-art performance in objective assessments.

Produces high-quality fused images with enhanced feature preservation.

Demonstrates robustness across various image datasets.

Abstract

In recent years, deep learning has become a very active research tool which is used in many image processing fields. In this paper, we propose an effective image fusion method using a deep learning framework to generate a single image which contains all the features from infrared and visible images. First, the source images are decomposed into base parts and detail content. Then the base parts are fused by weighted-averaging. For the detail content, we use a deep learning network to extract multi-layer features. Using these features, we use l_1-norm and weighted-average strategy to generate several candidates of the fused detail content. Once we get these candidates, the max selection strategy is used to get final fused detail content. Finally, the fused image will be reconstructed by combining the fused base part and detail content. The experimental results demonstrate that our proposed method achieves state-of-the-art performance in both objective assessment and visual quality. The Code of our fusion method is available at https://github.com/hli1221/imagefusion_deeplearning