DEEPBLUR: BLIND IDENTIFICATION OF SPACE VARIANT PSF
Résumé
We propose to train a neural network to estimate space varying blur operators from a single blurry image. The key assumption is that the operator lives in a subset of a known subspace, which is a reasonable assumption in many microscopes. We detail a specific sampling procedure of the subset to train a Resnet architecture. This allows a fast estimation. We finally illustrate the performance of the network on de-blurring problems. Index Terms-blur identification, neural network, non-uniform blur, blind debluring, blind inverse problem.
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