In recent years, classical knowledge-driven approaches for inverse problems have been complemented by data-driven methods exploiting the power of machine and especially deep learning. Purely data-driven methods, howev...
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Group equivariant convolutional neural networks (G-CNNs) have been successfully applied in geometric deep learning. the recently introduced framework of PDE-based G-CNNs (PDE-G-CNNs) generalizes G-CNNs while simultane...
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the spatial resolution of images of living samples obtained by fluorescence microscopes is physically limited due to the diffraction of visible light, which makes the study of entities of size less than the diffractio...
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Blind deconvolution involves the estimation of a sharp signal or image given only a blurry observation. Because this problem is fundamentally ill-posed, strong priors on boththe sharp image and blur kernel are requir...
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