IEEE Geoscience and Remote Sensing Magazine - March 2023 - 43

40. We employ five other representative approaches based
on the mixed-noise degradation model to evaluate the
denoising performance, quantitatively, containing LRTR
(with TNN), LRTDTV (using Tucker decomposition with
TV), LRTDGS (using Tucker decomposition with sparsityregularized
TV), three-directional TNN (using TNN), and
TL LL
RT (withT .VV)
- 00
In the preceding, we showed specific noise configurations
for different noise cases to compare the performance
of denoised approaches through several quantitative quality
assessments. Real-world data experiments can be carried
out when one denoising algorithm performs well in different
simulated data experiments. The origin of the simulation
and real datasets are listed in the second row of Table 2.
Ten denoising methods adapt their own decompositions
without/with different priors, with their code links in Table 1.
Four different quantitative quality indices are chosen: the
mean of the peak signal-to-noise ratio (MPSNR), mean of
structural similarity (MSSIM), relative dimensional global
error in synthesis (ERGAS), and mean spectral angle distance

IEEE Geoscience and Remote Sensing Magazine - March 2023

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