On 22 November 2017 23:08:17 Jérôme Kieffer wrote:
On Thu, 23 Nov 2017 12:33:21 +1100
Juan Nunez-Iglesias wrote:
Sounds great! I’ll admit that I’m not confident in this area either,
but my reading of the documentation suggests that this is the right
approach: the damping controls the square
Hi Juan,
On Thu, 23 Nov 2017 12:33:21 +1100
Juan Nunez-Iglesias wrote:
> Sounds great! I’ll admit that I’m not confident in this area either,
> but my reading of the documentation suggests that this is the right
> approach: the damping controls the square norm of x. By keeping it
> small (with
Hi Jerome
The problem you describe sounds very similar to the one solved for
super-resolution imaging.
My PhD thesis talks quite a bit about the methods utilized to solve it:
http://mentat.za.net/phd_dissertation.html
The code is here, but---written by some PhD student in 2009 ;)
https://github.
Hi Jérôme,
Sounds great! I’ll admit that I’m not confident in this area either, but my
reading of the documentation suggests that this is the right approach: the
damping controls the square norm of x. By keeping it small (with large
damping), you force the elements to be non-negative.
I hope t
On Wed, 22 Nov 2017 12:40:50 +1100
Juan Nunez-Iglesias wrote:
> Hi Jérôme,
>
> Can you explain your problem more? You know A and x and want to find
> b? Is this an exact solution, or is Ax = b + err? SciPy’s
> sparse.linalg module is where you’ll find most of your answers, I
> think… If you want
Hi Jérôme,
Can you explain your problem more? You know A and x and want to find b? Is this
an exact solution, or is Ax = b + err? SciPy’s sparse.linalg module is where
you’ll find most of your answers, I think… If you want to *build* A from some
description, you might find our homography exampl
Dear all,
I have an image which is "blurred" by a kernel which depends on the
position on the image.
This blurring can be expressed as a sparse matrix (A) multiplication where
only the neighboring pixels have non-null contribution.
Ax = b
where in addition
Aij>=0
x >= 0 #non negativity constra
