Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Medical image super-resolution method based on dense blended attention
  network   In order to address the issue that medical image would suffer from severe
blurring caused by the lack of high-frequency details in the process of image
super-resolution reconstruction, a novel medical image super-resolution method
based on dense neural network and blended attention mechanism is proposed. The
proposed method adds blended attention blocks to dense neural
network(DenseNet), so that the neural network can concentrate more attention to
the regions and channels with sufficient high-frequency details. Batch
normalization layers are removed to avoid loss of high-frequency texture
details. Final obtained high resolution medical image are obtained using
deconvolutional layers at the very end of the network as up-sampling operators.
Experimental results show that the proposed method has an improvement of 0.05db
to 11.25dB and 0.6% to 14.04% on the peak signal-to-noise ratio(PSNR) metric
and structural similarity index(SSIM) metric, respectively, compared with the
mainstream image super-resolution methods. This work provides a new idea for
theoretical studies of medical image super-resolution reconstruction.

14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio