EAT: Multi-Exposure Image Fusion With Adversarial Learning and Focal Transformer
Wei Tang, Fazhi He
Abstract
In this article, different from previous traditional multi-exposure image fusion (MEF) algorithms that use hand-designed feature extraction approaches or deep learning-based algorithms that utilize convolutional neural networks for information preservation, we propose a novel multi-Exposure image fusion method via Adversarial learning and focal Transformer, named EAT. In our framework, a Focal Transformer is proposed to focus on more remarkable regions and construct long-range multi-exposure relationships, with which the fusion model can simultaneously extract local and global multi-exposure properties and therefore generate promising fusion results. To further improve the fusion performance, we introduce adversarial learning to train the proposed method in an adversarial manner with the guidance of ground truth. By doing so, the fused images exhibit better visual perception and color fidelity. Extensive experiments conducted on publicly available databases provide compelling evidence that EAT surpasses other state-of-the-art approaches on both quantitative and qualitative evaluations. Furthermore, we directly employ our trained model to address another benchmark MEF dataset. The impressive fusion performance serves as evidence of the credible generalization ability of EAT.