Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning a Discriminative Prior for Blind Image Deblurring   We present an effective blind image deblurring method based on a data-driven
discriminative prior.Our work is motivated by the fact that a good image prior
should favor clear images over blurred images.In this work, we formulate the
image prior as a binary classifier which can be achieved by a deep
convolutional neural network (CNN).The learned prior is able to distinguish
whether an input image is clear or not.Embedded into the maximum a posterior
(MAP) framework, it helps blind deblurring in various scenarios, including
natural, face, text, and low-illumination images.However, it is difficult to
optimize the deblurring method with the learned image prior as it involves a
non-linear CNN.Therefore, we develop an efficient numerical approach based on
the half-quadratic splitting method and gradient decent algorithm to solve the
proposed model.Furthermore, the proposed model can be easily extended to
non-uniform deblurring.Both qualitative and quantitative experimental results
show that our method performs favorably against state-of-the-art algorithms as
well as domain-specific image deblurring approaches.

19
The categories of this paper are: eess
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an