Transform Domain

Transform domain methods leverage mathematical transformations to represent data (images, signals, etc.) in alternative spaces, often improving processing efficiency or revealing hidden patterns. Current research focuses on applying these techniques within deep learning architectures, such as using wavelet packets for subspace clustering, CycleGANs for image-to-image translation (e.g., in medical imaging), and DCT-based layers to enhance convolutional neural networks. These approaches aim to improve model performance, reduce computational costs, and enhance the interpretability of results across diverse applications, including medical image analysis, signal processing, and pattern recognition.