The high heterogeneity of complex backgrounds destroys the low rank of a scene, and it is difficult for existing algorithms to use low-rank sparse recovery methods to separate dim targets from the background. To resolve this problem, this study transforms the dim target detection problem into a convex optimization function-solving problem for tensor models. It proposes a detection model based on sparsely enhanced reweighting and mask patch tensors. First, the stacked mask patch image was expanded into a tensor space, and a mask patch-tensor model was constructed to filter the candidate targets. Thus, a sparse enhanced reweighting model was constructed using structural tensors to suppress background clutter, and the limitation of setting the weighting parameters can be overcome by solving convex optimization functions. The experiments show that the proposed algorithm outperforms recent representative algorithms regarding the background suppression factor and signal-to-noise ratio gain, demonstrating its effectiveness.