We propose a shallow neural network to perform efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm.
In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where ...
Jul 12, 2021 · This paper proposes a new two-step procedure for sparse-view tomographic image reconstruction. It is called RISING, since it combines an early- ...
Jul 12, 2021 · We here propose a shallow neural network to perform an efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection ...
In this paper, we propose a new deep learning approach based on unfolded neural networks for the reconstruction of X-ray computed tomography images from few ...
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A new two-step procedure for sparse-view tomographic image reconstruction is proposed, called RISING, since it combines an early-stopped Rapid Iterative ...
Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, ...
Our extensive numerical experiments showcase the potential of our method to accurately recover the image and the projection angle distribution under noise ...
Missing: Prospective Post-
Dec 2, 2024 · A green prospective for learned post-processing in sparse-view tomographic reconstruction. Journal of Imaging, 2021. [31] E. Morotti, D ...
PAT reconstruction based on deep learning mainly includes post-processing methods, which use a network to eliminate artifacts in the images reconstructed by ...
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