Generally

traditional compressed sensing (CS) image recovery methods build the objective optimization function by using the signal sparsity in some specific feature spaces.They do not fully take the local features and structural properties of signal into account

which leads to constraints of the recovery performance and flexibility.In this paper

considering the non-local self-similarity (NLSS) in images

we propose an image CS reconstruction method based on the image low-rank property by converting the CS recovery problem into a matrix rank minimization problem of aggregating similar image patches.The proposed algorithm builds optimization model under the constraint of minimal recovery errors and employs the weighed nuclear norm minimization (WNNM) method to solve the low-rank optimization problem.By taking advantage of them

the proposed method exploits the self-information and structured sparse characteristics of the image very well

and therefore provides a better protection of image structures and textures.Experiments on different test images under various sampling rates have shown the effectiveness of the proposed algorithm.Especially

for richly-textured images

our method outperforms the art-of-the-state algorithms significantly under low sampling rates.