Image Fusion Network

Image fusion networks aim to combine information from multiple image sources (e.g., infrared and visible light, multiple camera views) into a single, enhanced image that surpasses the quality of individual inputs. Current research emphasizes developing efficient architectures, such as those incorporating transformers and convolutional neural networks, to handle diverse fusion tasks and improve feature extraction. These advancements are driving progress in applications ranging from medical imaging and remote sensing to robotics and autonomous systems, where improved image quality leads to better decision-making and analysis. A key focus is on developing methods that are robust to varying conditions, such as low-light environments or occlusions, and that can effectively integrate semantic and object-level information.