A visible and infrared image fusion framework based on dual-path encoder-decoder and multi-scale discrete wavelet transform

In recent years, extensive research has been conducted on visible and infrared image fusion (VIF) task using traditional multi-scale transform-based and deep learning model-based methods. However, there is still a need to explore the combination of neural networks and multi-scale transform. This paper proposes a novel fusion framework based on a dual-path encoder-decoder and multi-scale transform. A dual-path encoder is trained to extract rich features at different depths from source images, while a shared decoder is trained to efficiently reconstruct images from the extracted feature space. We apply the discrete wavelet transform (DWT) to generate various frequency components from the extracted features. A fusion module is utilized to achieve fusion for low and high-frequency sub-bands, respectively, which is constrained by a gradient-based fusion loss function and an absolute values maximum-selection strategy. Our proposed method is superior to current state-of-the-art fusion methods, as demonstrated through quantitative and qualitative comparisons of publicly available datasets.