Infrared/Visible Light Fire Image Fusion Method Based on Generative Adversarial Network of Wavelet-Guided Pooling Vision Transformer

Abstract

To address issues of detail loss, limited matching datasets, and low fusion accuracy in infrared/visible light fire image fusion, a novel method based on the Generative Adversarial Network of Wavelet-Guided Pooling Vision Transformer (VTW-GAN) is proposed. The algorithm employs a generator and discriminator network architecture, integrating the efficient global representation capability of Transformers with wavelet-guided pooling for extracting finer-grained features and reconstructing higher-quality fusion images. To overcome the shortage of image data, transfer learning is utilized to apply the well-trained model to fire image fusion, thereby improving fusion precision. The experimental results demonstrate that VTW-GAN outperforms the DenseFuse, IFCNN, U2Fusion, SwinFusion, and TGFuse methods in both objective and subjective aspects. Specifically, on the KAIST dataset, the fusion images show significant improvements in Entropy (EN), Mutual Information (MI), and Quality Assessment based on Gradient-based Fusion (Qabf) by 2.78%, 11.89%, and 10.45%, respectively, over the next-best values. On the Corsican Fire dataset, compared to data-limited fusion models, the transfer-learned fusion images enhance the Standard Deviation (SD) and MI by 10.69% and 11.73%, respectively, and compared to other methods, they perform well in Average Gradient (AG), SD, and MI, improving them by 3.43%, 4.84%, and 4.21%, respectively, from the next-best values. Compared with DenseFuse, the operation efficiency is improved by 78.3%. The method achieves favorable subjective image outcomes and is effective for fire-detection applications.