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Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI
March 18, 2024, 4:45 a.m. | Chong Wang, Lanqing Guo, Yufei Wang, Hao Cheng, Yi Yu, Bihan Wen
cs.CV updates on arXiv.org arxiv.org
Abstract: Deep unfolding networks (DUN) have emerged as a popular iterative framework for accelerated magnetic resonance imaging (MRI) reconstruction. However, conventional DUN aims to reconstruct all the missing information within the entire null space in each iteration. Thus it could be challenging when dealing with highly ill-posed degradation, usually leading to unsatisfactory reconstruction. In this work, we propose a Progressive Divide-And-Conquer (PDAC) strategy, aiming to break down the subsampling process in the actual severe degradation and …
abstract arxiv cs.cv eess.iv framework however imaging information iteration iterative mri networks null popular space type via
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