Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A HVS-inspired Attention to Improve Loss Metrics for CNN-based
  Perception-Oriented Super-Resolution   Deep Convolutional Neural Network (CNN) features have been demonstrated to be
effective perceptual quality features. The perceptual loss, based on feature
maps of pre-trained CNN's has proven to be remarkably effective for CNN based
perceptual image restoration problems. In this work, taking inspiration from
the the Human Visual System (HVS) and visual perception, we propose a spatial
attention mechanism based on the dependency human contrast sensitivity on
spatial frequency. We identify regions in input images, based on the underlying
spatial frequency, which are not generally well reconstructed during
Super-Resolution but are most important in terms of visual sensitivity. Based
on this prior, we design a spatial attention map that is applied to feature
maps in the perceptual loss and its variants, helping them to identify regions
that are of more perceptual importance. The results demonstrate the our
technique improves the ability of the perceptual loss and contextual loss to
deliver more natural images in CNN based super-resolution.

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The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph