Speaker
Description
Hyperspectral image (HSI) denoising is an important preprocessing step for downstream applications. Fully characterizing the spatial-spectral priors of HSI is crucial for denoising tasks. In recent years, denoising methods based on low-rank subspaces have garnered widespread attention. In the low-rank matrix factorization framework, the restoration of HSI can be formulated as a task of recovering two subspace factors. However, hyperspectral images are inherently three-dimensional tensors, and transforming the tensor into a matrix for operations inevitably disrupts the spatial structure of the data. To address this issue and better capture the spatial-spectral priors of HSI, this paper proposes a modeling approach based on Tucker decomposition with subspace continuity prior parameterization. This data-driven and model-driven joint modeling mechanism has the following two advantages: 1) Tucker decomposition allows for the characterization of the low-rank properties across multiple dimensions of the hyperspectral image, leading to a more accurate representation of spectral priors; 2) Implicit neural representation tools enable the adaptive and precise characterization of the subspace factor continuity under Tucker decomposition, which we have discovered for the first time. Extensive experiments demonstrate that our method outperforms a series of competing methods.
