We present deep unfolding using the block iterative shrinkage thresholding algorithm (BISTA). The general idea of deep unfolding is that we interpret an iterative algorithm as a neural network and learn the weights through data-driven optimization. We then apply this to BISTA and thus present learned BISTA (LBISTA). We also present Analytical LBISTA, where we compute the analytical weight matrix through data-free optimization. Furthermore we provide two solution strategies to solve this data-free optimization problem.
- To test the code, we provided the conda environment env.yml. We highly recommend to use a conda virtual environment to install the dependencies. Additionally the cvxpy toolbox is necessary for some scripts.
- We provide the main scripts,
run***.py. These train the networks and evaluate the NMSE over iterations, reconstructions and the regularization parameters for a block sparse setting as well as a more specific mmv setting. - We also provide with
Example_Optimize_GenBlockCoheherence.ipynba jupyter-notebook, based on the cvxpy-package, to examine the two solution strategies to solve the data-free optimization problem.