Abstract: Seismic deconvolution is essential for extracting layer information from noisy seismic data, but it is an ill-posed problem with nonunique solutions. Inspired by classical optimization ...
Abstract: Conventional deconvolution methods utilize hand-crafted image priors to constrain the optimization. While deep-learning-based methods have simplified the optimization by end-to-end training, ...
Recovering images from blur depends on a point-spread function, stable frequency-domain utilities, and careful regularization. deconvolution provides known-PSF restoration, blind workflows, PSF/OTF ...
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