A Novel Image Compressive Sensing Approach with Column Sparse Prior

Compressive sensing is an international popular issue recently. In classical compressive sensing framework, the global sparse prior is employed to recovery image from the incomplete random projection, hence it is time consuming. In this paper we present a new image compressive sensing approach for image sparse recovery with column sparse prior. As opposed to compressive sensing, our model and algorithm based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to measurement and the size of sensing matrix can be greatly reduced. A new practical variant of GPSR algorithm is developed for the relevant optimization problems. To test our model and evaluate its performance, a series of experiments were performed in the paper. The experimental results indicated that our method can decrease the memory space cost and computational time on encoding and recovery.