Cross Modality Image

Cross-modality image processing focuses on integrating information from different image sources (e.g., visible light, infrared, lidar) to overcome limitations of individual modalities and create richer, more robust representations. Current research emphasizes developing advanced fusion networks, often incorporating self-supervised learning and techniques like diffusion models or modality-adaptive mixup, to improve object detection, segmentation, and tracking across diverse scenarios, including adverse weather conditions and unstructured environments. This field is significant for advancing applications in robotics, medical imaging, and surveillance, where combining data from multiple sensors enhances accuracy and reliability in challenging conditions.