Non-local sparsity has been widely concerned in image compressive sensing. Considering the difference of distribution characteristic of among group-based sparse coefficients of image, a new method for image compressive sensing reconstruction (ICSR) is proposed based on the z-scores standardized group sparse representation (ZSGSR). Here, the similar patch groups of the image are firstly extracted and decomposed by adaptive PCA dictionary, then the resulting coefficients are normalized using z-score standardization in component-wise, and used to regularize compressive sensing recovery with l 1 norm term. The reconstruction model is solved by splitting Bregman iteration and soft threshold shrinking algorithm. The z-score standardization in group-based transformation domain effectively can improve the sparse representation ability of the image and better restore the edges and texture details in ICSR. Using objective and subjective quality evaluation, extensive experimental results verify the effectiveness of this method.