Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Low-rank Tensor Dictionary Learning Method for Multi-spectral Images
  Denoising   As a 3-order tensor, a multi-spectral image (MSI) has dozens of spectral
bands, which can deliver more information for real scenes. However, real MSIs
are often corrupted by noises in the sensing process, which will further
deteriorate the performance of higher-level classification and recognition
tasks. In this paper, we propose a Low-rank Tensor Dictionary Learning (LTDL)
method for MSI denoising. Firstly, we extract blocks from the MSI and cluster
them into groups. Then instead of using the exactly low-rank model, we consider
a nearly low-rank approximation, which is closer to the latent low-rank
structure of the clean groups of real MSIs. In addition, we propose to learn an
spatial dictionary and an spectral dictionary, which contain the spatial
features and spectral features respectively of the whole MSI and are shared
among different groups. Hence the LTDL method utilizes both the latent low-rank
prior of each group and the correlation of different groups via the shared
dictionaries. Experiments on synthetic data validate the effectiveness of
dictionary learning by the LTDL. Experiments on real MSIs demonstrate the
superior denoising performance of the proposed method in comparison to
state-of-the-art methods.

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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
1
The categories of this paper are: cs
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The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: astro-ph
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The categories of this paper are: econ