Spectral Transformer

Spectral Transformers are a class of neural network architectures designed to leverage spectral information within data, improving upon traditional Transformers by incorporating frequency-domain analysis. Current research focuses on applying these models to diverse tasks, including hyperspectral image processing (classification, denoising, reconstruction), single-image deraining, and even astronomical data analysis for star property estimation, often integrating them with convolutional neural networks or other methods for enhanced performance. This approach demonstrates significant potential for improving the accuracy and efficiency of various applications by effectively capturing both local and long-range dependencies within complex datasets, particularly those with rich spectral characteristics.