Wavelet Diffusion

Wavelet diffusion models leverage the wavelet transform to improve the efficiency and performance of diffusion probabilistic models, primarily for image and 3D shape generation and restoration tasks. Current research focuses on developing novel architectures, such as teacher-student networks and uncertainty-guided approaches, to enhance image quality, speed up inference, and handle diverse data types including medical images and low-light conditions. This approach offers significant advantages in terms of computational efficiency and scalability, leading to improved results in various applications like image compression, super-resolution, and medical image synthesis.