| Abstract: |
| Phase Unwrapping (PU) is a fundamental task in MRI, InSAR, and fringe projection profilometry, serving as a critical step for recovering continuous physical quantities. Conventional PU algorithms rely heavily on the Itoh continuity condition, which often fails in experimental scenarios characterized by high noise, sharp discontinuities, or undersampling, leading to severe reconstruction artifacts.
This talk presents two novel unsupervised frameworks addressing these challenges. First, we introduce a nonconvex optimization model utilizing L1-L2 regularization and tail minimization. By enhancing gradient sparsity and targeting Itoh condition residuals, this approach significantly improves reconstruction accuracy for complex phase maps. Second, we integrate Deep Image Prior (DIP) with generalized Itoh conditions to establish an unsupervised, high-fidelity unwrapping framework that requires no pre-training datasets. Numerical and experimental results demonstrate that our proposed methods achieve superior signal-to-noise ratios and detail preservation compared to classical and supervised algorithms. These works offer robust and efficient computational solutions for challenging phase retrieval tasks in modern imaging. |
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