| Convolution with non-constant Kernel? [message #73468] |
Thu, 11 November 2010 16:56  |
SonicKenking
Messages: 51
Registered: October 2010
|
Member |
|
|
Hi, I wonder if there is an easy way to perform convolution on an
array with non-constant kernel.
The IDL built-in CONVOL function requires the kernel to be a fixed
array, e.g.
[-1,2,-1]. I want to have a dynamic kernel that changes based on the
position of the array. Something like
array = [8,6,7,9,1,3,4,5], kernel=[sin(index_i-1), 2, sin(index_i+1)]
Is there any other built-in IDL function that can do this or is there
someone who has already coded this up? If the answer is no, I'll go
ahead and code my own program. Just checking it here beforehand to
avoid re-inventing wheels.
Thanks!
|
|
|
|
| Re: Convolution with non-constant Kernel? [message #73516 is a reply to message #73468] |
Sat, 13 November 2010 08:23  |
Juggernaut
Messages: 83
Registered: June 2008
|
Member |
|
|
On Nov 12, 5:03 pm, chris <rog...@googlemail.com> wrote:
> On 12 Nov., 01:56, SonicKenking <ywa...@gmail.com> wrote:
>
>
>
>> Hi, I wonder if there is an easy way to perform convolution on an
>> array with non-constant kernel.
>
>> The IDL built-in CONVOL function requires the kernel to be a fixed
>> array, e.g.
>> [-1,2,-1]. I want to have a dynamic kernel that changes based on the
>> position of the array. Something like
>
>> array = [8,6,7,9,1,3,4,5], kernel=[sin(index_i-1), 2, sin(index_i+1)]
>
>> Is there any other built-in IDL function that can do this or is there
>> someone who has already coded this up? If the answer is no, I'll go
>> ahead and code my own program. Just checking it here beforehand to
>> avoid re-inventing wheels.
>
>> Thanks!
>
> If you are interested. I have a routine which strictly performs
> discrete convolutions for a 2d array and a 3d kernel without zero
> padding, without loops and for small kernels faster than fft. Just
> send me an email.
>
> Cheers
>
> CR
These types of algorithms would be useful for implementing a tilt-
shift photography on a standard image.
|
|
|
|
| Re: Convolution with non-constant Kernel? [message #73524 is a reply to message #73468] |
Fri, 12 November 2010 14:03 |
rogass
Messages: 200
Registered: April 2008
|
Senior Member |
|
|
On 12 Nov., 01:56, SonicKenking <ywa...@gmail.com> wrote:
> Hi, I wonder if there is an easy way to perform convolution on an
> array with non-constant kernel.
>
> The IDL built-in CONVOL function requires the kernel to be a fixed
> array, e.g.
> [-1,2,-1]. I want to have a dynamic kernel that changes based on the
> position of the array. Something like
>
> array = [8,6,7,9,1,3,4,5], kernel=[sin(index_i-1), 2, sin(index_i+1)]
>
> Is there any other built-in IDL function that can do this or is there
> someone who has already coded this up? If the answer is no, I'll go
> ahead and code my own program. Just checking it here beforehand to
> avoid re-inventing wheels.
>
> Thanks!
If you are interested. I have a routine which strictly performs
discrete convolutions for a 2d array and a 3d kernel without zero
padding, without loops and for small kernels faster than fft. Just
send me an email.
Cheers
CR
|
|
|
|
| Re: Convolution with non-constant Kernel? [message #73573 is a reply to message #73468] |
Fri, 19 November 2010 02:07 |
rogass
Messages: 200
Registered: April 2008
|
Senior Member |
|
|
Hi, try the following routines for 2D images and 2D mask or mask sized
[maskx,masky,n_elements(image)],whereas boundary pixels are not
considered, large images and small masks will cause heavy memory
usage:
function cr_discrete_convolve,mask,im
szm = ulong(size(mask,/dimensions))
szi = ulong(size(im,/dimensions))
return, reform(/
over,total(total(im[cr_window(szi[0],szi[1],szm[0],szm[1])]* $
n_elements(szm) eq 2? rebin(/
sample,mask,szm[0],szm[1],szi[0]*szi[1]):$
mask,2),1),szi[0],szi[1])
end
function cr_window,sx,sy,wx,wy,norot=norot
wx = ulong(wx)
wy = ulong(wy)
sx = ulong(sx)
sy = ulong(sy)
base = ((ll=ulindgen(wx,wy) mod wx)) + transpose(ll)*sx
return, rebin(/sample,(keyword_set(norot)? temporary(base) : $
rot(reform(/over,temporary(base),wx,wy),180)),wx,wy,sx*sy) + $
rebin(/sample,ulindgen(1,1,sx*sy),wx,wy,sx*sy)
end
Cheers
CR
|
|
|
|
| Re: Convolution with non-constant Kernel? [message #73597 is a reply to message #73468] |
Wed, 17 November 2010 12:52 |
Gray
Messages: 253
Registered: February 2010
|
Senior Member |
|
|
I'm trying to implement this sort of thing right now... the
approximation I'm using is to subdivide the image into pieces that are
small enough that the approximation of a constant kernel over a
subdivision is reasonably good (which for my images is around 128px
square in a grid of 16x16 subdivisions), then reconstruct into the
full image. This seems like it could be good enough for my purposes
(matching spatially-varying PSFs between two images), but I don't know
if it works for what you want.
|
|
|
|
comp.lang.idl-pvwave archive
Members
Search
Help
Login
Home
