Dual Domain

Dual-domain approaches in image and signal processing leverage information from both the spatial and frequency domains to improve model performance. Current research focuses on developing neural networks that effectively fuse features from these domains, often employing architectures like U-Nets, transformers, and iterative networks tailored to specific tasks. This dual-domain strategy has shown significant improvements in various applications, including medical image segmentation, remote sensing image enhancement, and low-dose computed tomography reconstruction, by addressing limitations of single-domain methods and achieving higher accuracy and efficiency. The resulting advancements promise to enhance the quality and speed of image and signal processing across diverse scientific and engineering fields.