  Infrared small target detection (ISTD) under complex backgrounds is a
difficult problem, for the differences between targets and backgrounds are not
easy to distinguish. Background reconstruction is one of the methods to deal
with this problem. This paper proposes an ISTD method based on background
reconstruction called Dynamic Background Reconstruction (DBR). DBR consists of
three modules: a dynamic shift window module (DSW), a background reconstruction
module (BR), and a detection head (DH). BR takes advantage of Vision
Transformers in reconstructing missing patches and adopts a grid masking
strategy with a masking ratio of 50\% to reconstruct clean backgrounds without
targets. To avoid dividing one target into two neighboring patches, resulting
in reconstructing failure, DSW is performed before input embedding. DSW
calculates offsets, according to which infrared images dynamically shift. To
reduce False Positive (FP) cases caused by regarding reconstruction errors as
targets, DH utilizes a structure of densely connected Transformer to further
improve the detection performance. Experimental results show that DBR achieves
the best F1-score on the two ISTD datasets, MFIRST (64.10\%) and SIRST
(75.01\%).
