Compressed Sensing Image Reconstruction Algorithm by Dictionary Learning

It is a challenging task to reconstruct images from compressed sensing measurement due to its implicit ill-posed property. In this paper, we propose an image reconstruction algorithm for compressed sensing image application based on the adaptive dictionary, which is learned from the reconstructed image itself. The sparsity level is enhanced since the sparse coding of overlapping image patches takes into account the local image features, and accordingly the quality of the reconstructed image is improved. In addition, linearization technique is also exploited to remove the computation of matrix inversion. Numerical experiments are conducted on several test images with a variety of sampling ratios. The results demonstrate that our proposed algorithm can efficiently reconstruct images from compressed sensing measurements and achieve more than 3dB gain averagely over the current state-of-art compressed sensing reconstruction algorithms.