Spectral Diffusion

Spectral diffusion leverages the spectral decomposition of data (e.g., images, graphs, time series) to model and generate data distributions, often employing diffusion probabilistic models. Current research focuses on applying this framework to diverse tasks, including image processing (pansharpening, super-resolution, compressive imaging), graph generation, and time series forecasting, utilizing architectures like spectral graph convolutions and incorporating techniques such as low-rank approximations and wavelet transformations for efficiency. These advancements improve the accuracy and efficiency of various applications, particularly in areas where handling high-dimensional or complex data is crucial, such as remote sensing and traffic prediction.