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Recently, deep neural networks (DNNs) have shown advantages in acceleratingoptimization algorithms. One approach is to unfold finite number of iterationsof conventional optimization algorithms and to learn parameters in thealgorithms. However, these are forward methods and are indeed neither iterativenor convergent. Here, we present a novel DNN-based convergent iterativealgorithm that accelerates conventional optimization algorithms. We train a DNNto yield parameters in scaled gradient projection method. So far, theseparameters have been chosen heuristically, but have shown to be crucial forgood empirical performance. In simulation results, the proposed methodsignificantly improves the empirical convergence rate over conventionaloptimization methods for various large-scale inverse problems in imageprocessing.

Empirically Accelerating Scaled Gradient Projection Using Deep Neural Network For Inverse Problems In Image Processing