Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Perturbation of the Eigenvectors of the Graph Laplacian: Application to
  Image Denoising   The original contributions of this paper are twofold: a new understanding of
the influence of noise on the eigenvectors of the graph Laplacian of a set of
image patches, and an algorithm to estimate a denoised set of patches from a
noisy image. The algorithm relies on the following two observations: (1) the
low-index eigenvectors of the diffusion, or graph Laplacian, operators are very
robust to random perturbations of the weights and random changes in the
connections of the patch-graph; and (2) patches extracted from smooth regions
of the image are organized along smooth low-dimensional structures in the
patch-set, and therefore can be reconstructed with few eigenvectors.
Experiments demonstrate that our denoising algorithm outperforms the denoising
gold-standards.

30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an