Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A statistical learning approach to color demosaicing   A statistical learning/inference framework for color demosaicing is
presented. We start with simplistic assumptions about color constancy, and
recast color demosaicing as a blind linear inverse problem: color parameterizes
the unknown kernel, while brightness takes on the role of a latent variable. An
expectation-maximization algorithm naturally suggests itself for the estimation
of them both. Then, as we gradually broaden the family of hypothesis where
color is learned, we let our demosaicing behave adaptively, in a manner that
reflects our prior knowledge about the statistics of color images. We show that
we can incorporate realistic, learned priors without essentially changing the
complexity of the simple expectation-maximization algorithm we started with.

34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
17
The categories of this paper are: q-fin