  Infrared small target detection is crucial for the efficacy of infrared
search and tracking systems. Current tensor decomposition methods emphasize
representing small targets with sparsity but struggle to separate targets from
complex backgrounds due to insufficient use of intrinsic directional
information and reduced target visibility during decomposition. To address
these challenges, this study introduces a Sparse Differential Directionality
prior (SDD) framework. SDD leverages the distinct directional characteristics
of targets to differentiate them from the background, applying mixed sparse
constraints on the differential directional images and continuity difference
matrix of the temporal component, both derived from Tucker decomposition. We
further enhance target detectability with a saliency coherence strategy that
intensifies target contrast against the background during hierarchical
decomposition. A Proximal Alternating Minimization-based (PAM) algorithm
efficiently solves our proposed model. Experimental results on several
real-world datasets validate our method's effectiveness, outperforming ten
state-of-the-art methods in target detection and clutter suppression. Our code
is available at https://github.com/GrokCV/SDD.
