Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Image Deblurring: A Survey   Image deblurring is a classic problem in low-level computer vision, which
aims to recover a sharp image from a blurred input image. Recent advances in
deep learning have led to significant progress in solving this problem, and a
large number of deblurring networks have been proposed. This paper presents a
comprehensive and timely survey of recently published deep-learning based image
deblurring approaches, aiming to serve the community as a useful literature
review. We start by discussing common causes of image blur, introduce benchmark
datasets and performance metrics, and summarize different problem formulations.
Next we present a taxonomy of methods using convolutional neural networks (CNN)
based on architecture, loss function, and application, offering a detailed
review and comparison. In addition, we discuss some domain-specific deblurring
applications including face images, text, and stereo image pairs. We conclude
by discussing key challenges and future research directions.

17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics