Aiming at the problems of the traditional Robust Principal Component Analysis (RPCA) based infrared small target detection algorithm that is insensitive to noise,the algorithm runs for a long time,and the robustness is not strong,a re weighted infrared small target image model is proposed,and non precision enhancement is used,which is Wide Lagrangian multiplier method (AIALM)solution.This method first converts the original infrared image into an infrared block image model,and then uses a re weighted kernel norm to constrain the background block image,which better preserves the background edge.Aiming at the problem that using norm alone cannot suppress some noise or clutter,a weighted norm is introduced to further enhance the sparsity of the target image.Finally,the infrared block image model is transformed into a weighted RPCA problem and solved by AIALM.A large number of experiments show that this algorithm is better than other traditional algorithms in suppressing background clutter and target detection performance.