Infrared and Visible Image Fusion using a Deep Learning Framework
This addresses image fusion for applications like surveillance or medical imaging, but it is incremental as it builds on existing deep learning and decomposition techniques.
The paper tackles the problem of fusing infrared and visible images by proposing a deep learning framework that decomposes images into base and detail parts, fuses them using weighted-averaging and feature extraction, and reconstructs a single image with all features, achieving state-of-the-art performance in objective assessment and visual quality.
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