when facing extremely complex infrared background, due to the defect of 11 norm based sparsity measure, the state-of-the-art infrared patch-image (ipi) model would be in a dilemma where either the dim targets are over-shrinked in the separation or the strong cloud edges remains in the target image. in order to suppress the strong edges while preserving the dim targets, a weighted infrared patch image (wipi) model is proposed, incorporating structural prior information into the process of infrared small target and background separation. instead of adopting a global weight, we allocate adaptive weight to each column of the target patch-image according to its patch structure. then the proposed wipi model is converted to a column-wise weighted robust principal component analysis (cwrpca) problem. in addition, a target unlikelihood coefficient is designed based on the steering kernel, serving as the adaptive weight for each column. finally, in order to solve the cwprca problem, a solution algorithm is developed based on alternating direction method (adm). detailed experiment results demonstrate that the proposed method has a significant improvement over the other nine classical or state-of-the-art methods in terms of subjective visual quality, quantitative evaluation indexes and convergence rate. (c) 2016 elsevier b.v. all rights reserved.