Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nonlocal Patch-Based Fully-Connected Tensor Network Decomposition for
  Remote Sensing Image Inpainting   Remote sensing image (RSI) inpainting plays an important role in real
applications. Recently, fully-connected tensor network (FCTN) decomposition has
been shown the remarkable ability to fully characterize the global correlation.
Considering the global correlation and the nonlocal self-similarity (NSS) of
RSIs, this paper introduces the FCTN decomposition to the whole RSI and its NSS
groups, and proposes a novel nonlocal patch-based FCTN (NL-FCTN) decomposition
for RSI inpainting. Different from other nonlocal patch-based methods, the
NL-FCTN decomposition-based method, which increases tensor order by stacking
similar small-sized patches to NSS groups, cleverly leverages the remarkable
ability of FCTN decomposition to deal with higher-order tensors. Besides, we
propose an efficient proximal alternating minimization-based algorithm to solve
the proposed NL-FCTN decomposition-based model with a theoretical convergence
guarantee. Extensive experiments on RSIs demonstrate that the proposed method
achieves the state-of-the-art inpainting performance in all compared methods.

19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph