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Signal models based on sparse representation have received considerableattention in recent years. Compared to synthesis dictionary learning,sparsifying transform learning involves highly efficient sparse coding andoperator update steps. In this work, we propose a Multi-layer ResidualSparsifying Transform (MRST) learning model wherein the transform domainresiduals are jointly sparsified over layers. In particular, the transforms forthe deeper layers exploit the more intricate properties of the residual maps.We investigate the application of the learned MRST model for low-dose CTreconstruction using Penalized Weighted Least Squares (PWLS) optimization.Experimental results on Mayo Clinic data show that the MRST model outperformsconventional methods such as FBP and PWLS methods based on edge-preserving (EP)regularizer and single-layer transform (ST) model, especially for maintainingsome subtle details.

Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction