  The existence of hybrid noise in hyperspectral images (HSIs) severely
degrades the data quality, reduces the interpretation accuracy of HSIs, and
restricts the subsequent HSIs applications. In this paper, the spatial-spectral
gradient network (SSGN) is presented for mixed noise removal in HSIs. The
proposed method employs a spatial-spectral gradient learning strategy, in
consideration of the unique spatial structure directionality of sparse noise
and spectral differences with additional complementary information for better
extracting intrinsic and deep features of HSIs. Based on a fully cascaded
multi-scale convolutional network, SSGN can simultaneously deal with the
different types of noise in different HSIs or spectra by the use of the same
model. The simulated and real-data experiments undertaken in this study
confirmed that the proposed SSGN performs better at mixed noise removal than
the other state-of-the-art HSI denoising algorithms, in evaluation indices,
visual assessments, and time consumption.
