[Faculty] Fwd: [CSRC.COLLOQUIUM] "BATUD: Blind Atmospheric Turbulence Deconvolution"

[Jose Castillo]

[image: SDSU_CSRC Logo.jpg]





DATE:
*Friday, June 12, 2020*



TITLE:

*BATUD: Blind Atmospheric Turbulence Deconvolution*


TIME:
*3:00-4:00PM*




LOCATION:
Join Zoom Meeting -  *https://SDSU.zoom.us/j/96847180420*
<https://sdsu.zoom.us/j/96847180420>



SPEAKER/BIO:

*Dr. Jerome Gilles, Department of Mathematics and Statistics, San Diego
State University*





ABSTRACT:

In a previous work, we suggested using the Fried kernel to perform image
deconvolution of stabilized images acquired through atmospheric turbulence.
Despite that this approach provides very good results, it has a major
drawback: four parameters must be provided. If it is reasonable to consider
that three of them can be known, the fourth one (the C_n^2 describing the
turbulence intensity in the atmosphere) is most challenging one to guess.
In our original work, we used some brute force approach to estimate that
parameter making the method computationally very expansive and very hard to
use in practical applications. In this new work, we propose to reformulate
the Fried kernel in a simpler way. The new formulation is more flexible and
only have two parameters which can easily be estimated via a Newton descent
approach. We then use a procedure which alternates a very fast
state-of-the-art deconvolution algorithm with the optimization of the new
Fried kernel parameters. We show, both on simulated and real images, that
our algorithm has good performance (it generally over performed all
existing approaches in the case of strong turbulence) and is really fast.




Host: Jose Castillo

Note: Videos of previous colloquium talks can be seen on the CSRC website
in the colloquium archive section or on the CSRC YouTube page here
<https://www.youtube.com/channel/UCN0ZEztlmyDqG2pm-Rle_Eg/feed>.

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