Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Disentangled Feature Representation for Hybrid-distorted Image
  Restoration   Hybrid-distorted image restoration (HD-IR) is dedicated to restore real
distorted image that is degraded by multiple distortions. Existing HD-IR
approaches usually ignore the inherent interference among hybrid distortions
which compromises the restoration performance. To decompose such interference,
we introduce the concept of Disentangled Feature Learning to achieve the
feature-level divide-and-conquer of hybrid distortions. Specifically, we
propose the feature disentanglement module (FDM) to distribute feature
representations of different distortions into different channels by revising
gain-control-based normalization. We also propose a feature aggregation module
(FAM) with channel-wise attention to adaptively filter out the distortion
representations and aggregate useful content information from different
channels for the construction of raw image. The effectiveness of the proposed
scheme is verified by visualizing the correlation matrix of features and
channel responses of different distortions. Extensive experimental results also
prove superior performance of our approach compared with the latest HD-IR
schemes.

5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph