Regularization is widely used for solving ill-posed image reconstruction problems and an appropriate selection of the regularization parameter is critical in ensuring high-quality reconstructions. While many methods have been proposed to address this problem, selecting a regularization parameter for optimal performance (under a specific metric) in a computationally efficient manner is still an open problem. We propose here a novel deep learning based method for regularization parameter selection. Specifically, a convolutional neural network is designed to predict the optimal parameter from an “arbitrary” initial parameter choice. The proposed method has been evaluated using experimental data, demonstrating its capability to learn the optimal parameter for two different L1-regularized reconstruction problems.