Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust photon-efficient imaging using a pixel-wise residual shrinkage
  network   Single-photon light detection and ranging (LiDAR) has been widely applied to
3D imaging in challenging scenarios. However, limited signal photon counts and
high noises in the collected data have posed great challenges for predicting
the depth image precisely. In this paper, we propose a pixel-wise residual
shrinkage network for photon-efficient imaging from high-noise data, which
adaptively generates the optimal thresholds for each pixel and denoises the
intermediate features by soft thresholding. Besides, redefining the
optimization target as pixel-wise classification provides a sharp advantage in
producing confident and accurate depth estimation when compared with existing
research. Comprehensive experiments conducted on both simulated and real-world
datasets demonstrate that the proposed model outperforms the state-of-the-arts
and maintains robust imaging performance under different signal-to-noise ratios
including the extreme case of 1:100.

36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin