Infrared small target detection plays a vital role in defense monitoring and aerospace early warning. However, due to the target’s small size and the low signal-to-noise ratio, it remains challenging in complex backgrounds. The low-rank sparse decomposition (LRSD) methods achieve detection by modeling structural differences between targets and backgrounds. Nevertheless, most existing methods only impose global low-rank and local smoothness constraints on the background from an additive perspective, ignoring their coupling relationship. This limits their ability to accurately characterize complex backgrounds. Moreover, the commonly used isotropic \(L_1\) norm fails to fully exploit the multi-directional gradient responses of targets. To address these issues, this paper proposes an improved LRSD model called Global Joint Local with Multi-Directional Weighted (GJL-MDW). It introduces a joint regularization to simultaneously capture global and local background structures, and a multi-directional weighted \(L_1\) norm to describe target sparsity, adaptively enhancing target response while suppressing background interference. Experiments on public datasets demonstrate that GJL-MDW outperforms state-of-the-art baseline methods in both detection accuracy and robustness.