Complex Wavelet

Complex wavelet transforms are increasingly used in signal and image processing to analyze data across multiple scales and orientations, offering advantages in shift-invariance and detail preservation compared to traditional methods. Current research focuses on integrating complex wavelets into deep learning architectures, such as U-Nets and convolutional neural networks, for applications like image inpainting detection, medical image segmentation, and dynamic scene rendering. These advancements improve the accuracy and efficiency of various tasks, impacting fields ranging from medical diagnostics (e.g., seizure detection) to computer vision (e.g., defogging) and anomaly detection in textured images.