HSI Denoising

Hyperspectral image (HSI) denoising aims to remove noise from HSIs, improving the quality of data crucial for various applications like remote sensing and medical imaging. Current research emphasizes developing advanced deep learning models, including transformers and generative adversarial networks (GANs), often incorporating techniques like state-space models and low-rank tensor regularization to effectively capture spatial-spectral correlations and handle complex noise patterns. These improvements lead to more accurate and efficient denoising, enhancing the reliability of HSI-based analyses and downstream tasks. The development of open-source toolboxes further facilitates broader access and application of these techniques.