Global Enhancement

Global enhancement techniques in image and signal processing aim to improve the quality and detail of various data types, addressing limitations in original acquisitions or enhancing specific features. Current research focuses on developing sophisticated models, including transformers and GANs, often incorporating wavelet transforms and attention mechanisms to achieve both local and global refinements, adapting to diverse data modalities (e.g., images, videos, 3D models). These advancements have significant implications for various fields, improving the accuracy of applications such as medical imaging, autonomous driving, and 3D modeling by mitigating noise, enhancing resolution, and improving feature extraction.