Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-supervised Hyperspectral Image Restoration using Separable Image
  Prior   Supervised learning with a convolutional neural network is recognized as a
powerful means of image restoration. However, most such methods have been
designed for application to grayscale and/or color images; therefore, they have
limited success when applied to hyperspectral image restoration. This is
partially owing to large datasets being difficult to collect, and also the
heavy computational load associated with the restoration of an image with many
spectral bands. To address this difficulty, we propose a novel self-supervised
learning strategy for application to hyperspectral image restoration. Our
method automatically creates a training dataset from a single degraded image
and trains a denoising network without any clear images. Another notable
feature of our method is the use of a separable convolutional layer. We
undertake experiments to prove that the use of a separable network allows us to
acquire the prior of a hyperspectral image and to realize efficient
restoration. We demonstrate the validity of our method through extensive
experiments and show that our method has better characteristics than those that
are currently regarded as state-of-the-art.

37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin