5 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…
The combination of CT and MRI can provide more accurate images of lesions, yielding a significantly higher diagnostic value compared to single-modality pathological images. However, in CT-MRI fusion, preserving the gray-scale distribution of the source image while avoiding ‘detail halos’ poses a cha…
In this paper, we addressed facial aesthetic enhancement (FAE). Although existing methods have made great progress, the beautified images generated by them are highly prone to poor beautification, which limits their application to real-world scenes. To tackle this problem, we proposed a new method c…