Frequency Prior

Frequency priors are statistical representations of the frequency components within data, used to improve the performance of various machine learning models, particularly in image processing tasks. Current research focuses on integrating frequency priors into deep learning architectures, such as diffusion models and transformers, to enhance image reconstruction, particularly in challenging scenarios like hyperspectral imaging and HDR deghosting. This approach leverages the observation that low-frequency information often correlates strongly with perceptually important features, leading to improved efficiency and visual quality. The development and application of effective frequency priors are significantly impacting the accuracy and speed of image generation and reconstruction across diverse applications.