Multivariate pursuit image reconstruction using prior information beyond sparsity

The prior information of images plays an important role in reducing the computational complexity of CS inversion and improving the reconstruction quality. A wavelet-based multivariate pursuit algorithm, which exploits the prior information of images that goes beyond simple sparsity, is developed in this paper. The proposed method reconstructs the image wavelet coefficients from the multiple measurements in a multivariate manner, and uses the extracted image edge as the prior information to guide the pursuit process of algorithm in CS recovery. By means of the interaction of edge information and multivariate joint recovery, the proposed algorithm significantly improves the reconstruction quality of those images with the obvious edges and high sparsity, such as CT, MRI images. Numerical experiments demonstrate that the proposed algorithm returns superior reconstructed quality and remains higher computational efficiency than other state-of-the-art CS algorithms.

► The wavelet-based CS reconstruction is converted to a jointly sparse recovery.
► The extracted edge is used as the prior for image CS reconstruction.
► A fast edge-based multivariate pursuit algorithm (EMPA) is proposed.
► The interaction of edge and joint recovery contributes a lot to reconstruction.
► EMPA has shown a good performance under low CS measurement rates.