Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CSMCNet: Scalable Video Compressive Sensing Reconstruction with
  Interpretable Motion Estimation   Most deep network methods for compressive sensing reconstruction suffer from
the black-box characteristic of DNN. In this paper, a deep neural network with
interpretable motion estimation named CSMCNet is proposed. The network is able
to realize high-quality reconstruction of video compressive sensing by
unfolding the iterative steps of optimization based algorithms. A DNN based,
multi-hypothesis motion estimation module is designed to improve the
reconstruction quality, and a residual module is employed to further narrow
down the gap between re-construction results and original signal in our
proposed method. Besides, we propose an interpolation module with corresponding
training strategy to realize scalable CS reconstruction, which is capable of
using the same model to decode various compression ratios. Experiments show
that a PSNR of 29.34dB can be achieved at 2% CS ratio (compressed by 98%),
which is superior than other state-of-the-art methods. Moreover, the
interpolation module is proved to be effective, with significant cost saving
and acceptable performance losses.

11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
1
The categories of this paper are: cs
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph