  In recent years, there has been a noteworthy focus on infrared small target
detection, given its vital importance in processing signals from infrared
remote sensing. The considerable computational cost incurred by prior methods,
relying excessively on nuclear norm for noise separation, necessitates the
exploration of efficient alternatives. The aim of this research is to identify
a swift and resilient tensor recovery method for the efficient extraction of
infrared small targets from image sequences. Theoretical validation indicates
that smaller singular values predominantly contribute to constructing noise
information. In the exclusion process, tensor QR decomposition is employed to
reasonably reduce the size of the target tensor. Subsequently, we address a
tensor $L_{2,1}$ Norm Minimization via T-QR (TLNMTQR) based method to
effectively isolate the noise, markedly improving computational speed without
compromising accuracy. Concurrently, by integrating the asymmetric
spatial-temporal total variation regularization method (ASSTV), our objective
is to augment the flexibility and efficacy of our algorithm in handling time
series data. Ultimately, our method underwent rigorous testing with real-world
data, affirmatively showcasing the superiority of our algorithm in terms of
speed, precision, and robustness.
