Filtered least squares and ADMM both accelerate INR-based sparse-view CT reconstruction, with ADMM giving the lowest final error on a simulated breast phantom.
Nonconvex Generalization of Alternating Direction Method of Multipliers for Nonlinear Equality Constrained Problems
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abstract
The classic Alternating Direction Method of Multipliers (ADMM) is a popular framework to solve linear-equality constrained problems. In this paper, we extend the ADMM naturally to nonlinear equality-constrained problems, called neADMM. The difficulty of neADMM is to solve nonconvex subproblems. We provide globally optimal solutions to them in two important applications. Experiments on synthetic and real-world datasets demonstrate excellent performance and scalability of our proposed neADMM over existing state-of-the-start methods.
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Accelerated Optimization of Implicit Neural Representations for CT Reconstruction
Filtered least squares and ADMM both accelerate INR-based sparse-view CT reconstruction, with ADMM giving the lowest final error on a simulated breast phantom.