2 research outputs found
Denoising of Hyperspectral Images Using Group Low-Rank Representation
Hyperspectral images (HSIs) have been used in a
wide range of fields, such as agriculture, food safety, mineralogy
and environment monitoring, but being corrupted by various
kinds of noise limits its efficacy. Low-rank representation (LRR)
has proved its effectiveness in the denoising of HSIs. However,
it just employs local information for denoising, which results
in ineffectiveness when local noise is heavy. In this paper, we
propose an approach of group low-rank representation (GLRR)
for the HSI denoising. In our GLRR, a corrupted HSI is divided
into overlapping patches, the similar patches are combined into
a group, and the group is reconstructed as a whole using LRR.
The proposed method enables the exploitation of both the local
similarity within a patch and the nonlocal similarity across the
patches in a group simultaneously. The additional nonlocallysimilar
patches can bring in extra structural information to the
corrupted patches, facilitating the detection of noise as outliers.
LRR is applied to the group of patches, as the uncorrupted
patches enjoy intrinsic low-rank structure. The effectiveness of
the proposed GLRR method is demonstrated qualitatively and
quantitatively by using both simulated and real-world data in
experiments
Hyperspectral image processing by jointly filtering wavelet component tensor
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