Advances in imaging technology have taken significant strides with the introduction of innovative image fusion models. A new study proposes such model aimed at enhancing the quality of multi-modal images, which is particularly valuable in fields such as medical imaging and surveillance. The model effectively integrates information from multiple image types, improving their usability for subsequent visual tasks.
Researchers have primarily focused on dual-stream methods employing convolutional neural networks (CNNs), finding limitations due to small receptive fields. This study proposes the use of both Transformers and CNNs, thereby uniting the strengths of each architecture. By improving information retention from image pairs—especially those comprising infrared and visible images as well as various medical images—the proposed model carries potential benefits across diverse practical applications.
To effectively engage features, the shared encoder framework is based on Transformers. The encoder includes intra-modal feature extraction blocks, inter-modal feature extraction blocks, and feature alignment blocks, all specially devised to manage slight misalignments between source images. For extracting both low- and high-frequency features, the model employs private encoders with dual-stream CNN architecture, enhancing its capability to model complex visual information.
A noteworthy advancement introduced with this model is the cross-attention-based Swin Transformer block, which enhances the exploration of cross-domain information. This is necessary for improving the efficiency of multi-modal image autonomy observed in various high-level tasks.
A unified loss function is also part of this innovative approach, incorporating dynamic weighting factors to accurately capture the inherent commonalities of the multi-modal images. This comprehensive structure has shown promise, as extensive qualitative and quantitative analyses demonstrate its superiority. The model is not only able to preserve thermal targets and background texture details but also exceeds the performance of existing state-of-the-art methods.
Training was conducted on the M3FD dataset, consisting of 2,700 high-resolution image pairs (1024x768), collected from typical urban road scenes useful for applications like autonomous driving. Verification on additional datasets supports the findings, demonstrating broad and stable applicability across distinct environments.
The research also utilized the Harvard Medical Dataset for the medical imaging models, highlighting its versatility across different domains. This comprehensive effort indicates the model's ability to integrate various image modalities, leading to improved outcomes especially for medical and surveillance applications.
Given the continual advances and challenges faced within the field of computer vision, the proposed model stands as a significant development, aiding future research and practical deployments of multi-modal image fusion technologies. With support from the National Natural Science Foundation of China, this work lays the groundwork for subsequent explorations, aiming for enhanced imaging techniques and synthesized understandings from multi-source inputs.