Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral
  Gradient Network   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.

29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
3
The categories of this paper are: physics
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin