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 l1 norm term. The reconstruction model
is solved by splitting Bregman iteration and soft threshold
shrinking algorithm. The z-score standardization in groupbased
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.