  To mitigate the issue of minimal intrinsic features for pure data-driven
methods, in this paper, we propose a novel model-driven deep network for
infrared small target detection, which combines discriminative networks and
conventional model-driven methods to make use of both labeled data and the
domain knowledge. By designing a feature map cyclic shift scheme, we modularize
a conventional local contrast measure method as a depth-wise parameterless
nonlinear feature refinement layer in an end-to-end network, which encodes
relatively long-range contextual interactions with clear physical
interpretability. To highlight and preserve the small target features, we also
exploit a bottom-up attentional modulation integrating the smaller scale subtle
details of low-level features into high-level features of deeper layers. We
conduct detailed ablation studies with varying network depths to empirically
verify the effectiveness and efficiency of the design of each component in our
network architecture. We also compare the performance of our network against
other model-driven methods and deep networks on the open SIRST dataset as well.
The results suggest that our network yields a performance boost over its
competitors. Our code, trained models, and results are available online.
