2019
The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems
ICASSP 2019accepted
In this work we investigate the practicability of stochastic gradient descent and recently introduced variants with variance-reduction techniques in imaging inverse problems, such as space-varying image deblurring. Such algorithms have been shown in machine learning literature to have optimal comple…