Compressed sensing based on dictionary updating and BM3D

What is it about?

we propose a novel MR image reconstruction algorithm utilising dictionary updating, which consists of three steps: sparse coding, dictionary updating and image reconstruction. In the dictionary updating step, they perform a first-order series expansion for dictionary coefficient matrix product via recursive method, and propose an efficient method to solve the new dictionary updating problem. To improve the reconstruction quality, the proposed BM3D regularisation is incorporated into the authors’ image CS recovery, which can combine the self-similarities within the image, the 3D transform sparsity and the local sparsity into image recovery process. Experimental simulations demonstrate their proposed algorithms can obtain better reconstruction quality than the previous CS algorithms.

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Why is it important?

To improve the CS reconstruction quality, the proposed BM3D regularisation is incorporated into the authors’ image CS recovery, which can combine the self-similarities within the image, the 3D transform sparsity and the local sparsity into image recovery process.