incDG inserts a deep-network estimate at every incremental TpV step, giving faster and more stable deblurring and CT reconstructions than either the pure model-based or pure deep-learning baselines, with slightly lower CT accuracy than the model-based solver.
Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized gauss–seidel methods
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An incremental algorithm for non-convex AI-enhanced medical image processing
incDG inserts a deep-network estimate at every incremental TpV step, giving faster and more stable deblurring and CT reconstructions than either the pure model-based or pure deep-learning baselines, with slightly lower CT accuracy than the model-based solver.