Image compressed sensing based on multi-level adaptive learning dictionaries

Sparse representation matrix is of great significance for Compressed Sensing (CS) reconstruction accuracy. Contourlet Transform (CT) offer a much richer set of directions and shapes, and it is more effective in capturing smooth contours and geometric structures in images. While dictionaries learned by machine learning methods can represent images more effectively. In this paper, we propose a multi-level adaptive dictionary learning (DL) strategy which combines both of the above advantages. We learn sub-dictionaries of high frequency of CT by an improved K-SVD algorithm, and moreover, the stopping criteria of sparse representation stage is associated with the iteratively updated dictionaries to get an adaptive sparse constraint, which gets more effective sparse representation coefficients and then improves the dictionary updating. This approach achieves a good reconstruction accuracy of the high frequency with less CS measurement. Experiment results demonstrate that the reconstructed images using dictionaries learned by the proposed algorithm in CS have better effect.