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Found 6 papers, 1 papers with code
Self-Supervised Isotropic Superresolution Fetal Brain MRI
In this paper, we sidestep this difficulty by providing a proof of concept of a self-supervised single-volume superresolution framework for T2-weighted FBMRI (SAIR).
Stability of Image-Reconstruction Algorithms
Robustness and stability of image-reconstruction algorithms have recently come under scrutiny.
Convex Quantization Preserves Logconcavity
A logconcave likelihood is as important to proper statistical inference as a convex cost function is important to variational optimization.
A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods
We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data.
Optimal-transport-based metric for SMLM
We propose the use of Flat Metric to assess the performance of reconstruction methods for single-molecule localization microscopy (SMLM) in scenarios where the ground-truth is available.
SpotNet - Learned iterations for cell detection in image-based immunoassays
Accurate cell detection and counting in the image-based ELISpot and FluoroSpot immunoassays is a challenging task.
