4 accepted papers
Deep unfolding networks (DUNs) have made significant progress in MRI reconstruction, successfully tackling the problem of prolonged imaging time. However, the ill-conditioned nature of MRI reconstruction often causes slow convergence in iterative optimization, potentially compromising the performanc…
Model-based networks have shown convincing performance in MRI reconstruction. However, the unrolled cascades within the networks are constrained to solely obtain information from the preceding counterpart, resulting in potential error accumulation. Moreover, the linear structure fails to address the…
In accelerated MRI reconstruction problem, directly recovering all the missing k-space data from undersampled measurements is highly ill-posed and often leads to suboptimal performance. To address the problem, we propose a novel deep unfolding network (DUN) with subsampling decomposition (SD) based…
Sparse Coding (SC) and Convolution Sparse Coding (CSC) are two widely studied sparse methods in computer vision and signal processing. SC encodes the image patches independently, however fails to utilize the correlation among them. CSC adopts a convolution operator to connect the overlapping patches…