it is enough information to get me going :)

I will take a close look at the link which by the way looks very impressive,

the approach was thinking was not conjugate gradient itself, but a variant

of constrained optimization:

http://www-fp.mcs.anl.gov/otc/Guide/SoftwareGuide/Categories/constropt.html

particularly the least squares solution, for which I'm familiar with:

http://www.sbsi-sol-optimize.com/products_lssol.htm

I do really appreciate your help, and hope to remain around

regards

Joel Rodr'iguez

P.S. wont bother for a while, thanks for your attention, yesterday's tequila,

some times make me feel that P=NP,..he,... :)

Ernst Lippe wrote:

On Thu, 19 Jun 2003 16:46:13 -0700 (PDT) Joel Eduardo Rodriguez Ramirez <[EMAIL PROTECTED]> wrote:## Advertising

Actually I would like to colaborate with a new filter also (as Bowie), but mine idea is in the direction of: ``Inverse Image Filtering with Conjugate Gradient'' http://people.cornell.edu/pages/zz25/imgcg/As far as I know nobody tried to implement conjugate gradient filtering as a GIMP plug-in. One of the main reasons I think is that it is not easy to give an efficient implementation. The running time is quadratic in the number of pixels in the image which means that it is too slow to use it on the normal sized images. I would expect that any realistic implementation should use the algorithm on small parts of the image and then somehow combine the results. So, this is not a trivial plug-in to write. Also you should not over-estimate what techniques like conjugate gradient filtering can do. The examples that most authors give are highly artificial. In the page that you referred to the convolution is known exactly and there was no noise in the blurred image. In real life this never happens. First of all you hardly ever know the exact details of the blurring convolution. Most deconvolution algorithms give very disappointing results unless you have a very good approximation of the blurring convolution. Second, almost all images contain noise and this has disastrous effect on the deconvolution. Most deconvolution algorithms tend to amplify the noise to an extreme degree. For example, when using the unmodified inverse deconvolution the end result is normally completely dominated by noise. Although conjugate gradient filtering is not so extremely sensitive to noise, like all deconvolution techniques its results will deteriorate rapidly even when there are only very small errors in the input. A few years ago, when I started with my own deconvolution plug-in I examined several existing algorithms. My own conclusion was that this is a difficult subject. Most of the best algorithms are simply too slow for practical applications. I finally selected FIR Wiener filtering as a practical compromise. The running time is linear in the number of pixels in the image, and in virtually all cases its results are much better than those of similar plug-ins like unsharp mask or sharpen. If you are interested you can find it at http://refocus.sourceforge.net. greetings, Ernst Lippe