Multi Resolution Fusion

Multi-resolution fusion integrates information from multiple sources at varying scales to improve accuracy and efficiency in diverse applications. Current research focuses on developing efficient algorithms, often employing neural network architectures like U-Nets and HRNets, to effectively fuse data from different sensors (e.g., cameras, lidar, radar) or resolutions within a single modality (e.g., image pyramids). This approach is proving valuable in various fields, enhancing performance in tasks such as image super-resolution, object detection, medical image analysis, and remote sensing by leveraging complementary information and mitigating limitations of individual data sources. The resulting improvements in accuracy and efficiency have significant implications for both scientific discovery and practical applications.