Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping
arxiv(2024)
摘要
This paper proposes a novel regularization method, named Spatio-Spectral
Structure Tensor Total Variation (S3TTV), for denoising and destriping of
hyperspectral (HS) images. HS images are inevitably contaminated by various
types of noise, during acquisition process, due to the measurement equipment
and the environment. For HS image denoising and destriping tasks,
Spatio-Spectral Total Variation (SSTV), defined using second-order
spatio-spectral differences, is widely known as a powerful regularization
approach that models the underlying spatio-spectral properties. However, since
SSTV refers only to adjacent pixels/bands, semi-local spatial structures are
not preserved during denoising process. To address this problem, we newly
design S3TTV, defined by the sum of the nuclear norms of matrices consisting of
second-order spatio-spectral differences in small spectral blocks (we call
these matrices as spatio-spectral structure tensors). The proposed
regularization method simultaneously models the spatial piecewise-smoothness,
the spatial similarity between adjacent bands, and the spectral correlation
across all bands in small spectral blocks, leading to effective noise removal
while preserving the semi-local spatial structures. Furthermore, we formulate
the HS image denoising and destriping problem as a convex optimization problem
involving S3TTV and develop an algorithm based on a preconditioned primal-dual
splitting method to solve this problem efficiently. Finally, we demonstrate the
effectiveness of S3TTV by comparing it with existing methods, including
state-of-the-art ones through denoising and destriping experiments.
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