TDiffDe: A Truncated Diffusion Model for Remote Sensing Hyperspectral
  Image Denoising
Jiang He Yajie Li Jie L Qiangqiang Yuan
Hyperspectral images play a crucial role in precision agriculture,
environmental monitoring or ecological analysis. However, due to sensor
equipment and the imaging environment, the observed hyperspectral images are
often inevitably corrupted by various noise. In this study, we proposed a
truncated diffusion model, called TDiffDe, to recover the useful information in
hyperspectral images gradually. Rather than starting from a pure noise, the
input data contains image information in hyperspectral image denoising. Thus,
we cut the trained diffusion model from small steps to avoid the destroy of
valid information.