Publications by authors named "Yury Korolev"
Article Synopsis
- This study focuses on deconvolution techniques in light-sheet microscopy, specifically addressing challenges caused by spatially varying blur and mixed noise types (Poisson and Gaussian).
- A variational model is proposed that blends these noise influences using a unique data fidelity term, enabling improved image reconstructions.
- The researchers demonstrate successful implementation of their model through the primal-dual hybrid gradient (PDHG) algorithm, yielding better results than existing methods on both simulated and real datasets.
We study variational regularisation methods for inverse problems with imperfect forward operators whose errors can be modelled by order intervals in a partial order of a Banach lattice. We carry out analysis with respect to existence and convex duality for general data fidelity terms and regularisation functionals. Both for and parameter choice rules, we obtain convergence rates of the regularised solutions in terms of Bregman distances.
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