Numerical methods for computational and data science (Imaging and Inverse Problems)

Inverse problems occurs when one wants to retrieve information of unknown quantities by indirect observations. Typical pathologies affecting these problems are non-existence / non-uniqueness of solutions and numerical instability. The general way to recover a usable and meaningful approximation of the unknown object is by means of regularization methods, which exploit a priori information on the features characterizing the object itself.
A classic example of inverse problem is the image deconvolution, in which the measured image is a corrupted version of the real distribution due to the finiteness of the acquisition system and the presence of statistical noise on the data.

Fireworks Galaxy NGC 6946: blurred image and reconstruction with the scaled gradient projection method (J. Sci. Comput. 65(3), 895-919, 2015).


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[Ultimo aggiornamento: 04/02/2021 08:09:20]