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 The problem is that even when you only have the smallest error in  your  "out" signal a direct inverse (or an LS estimate) may result in an unstable solution. This is often handled by introducing an uncertainty parameter (eg., a noise term) and then minimize the mean squared error which results in the Wiener filter. Essentially, the convolution operation destroys information that never can be recovered perfectly, but the Wiener filter does the best that you do (when using a linear method). /Fredrik Robert A. Macy wrote: ```Thank you for your suggestions and the paper, too. I tried putting in all kinds of nonzero values, even as large as .001 versus max of 1 and the thing still blows up I tried a series of things that don't work. My first idea was to convolve the guassiancurve [called gt]with some 'magical' function should then recreate the dirac function, or impluse function [called dt]. Now, how to find this magical function: the idea is that rt [magical function] convolved with gt equals dt. since convolution is identical to multiplying in the frequency domain.. Find the Fourier Transform for both gt and dt: then rf times gf equals df. dividing through finds rf = df/gf Well I first tried this with the offset where the peaks were at 51: for gaussian curve... ``` ```x=(-50:50); gt=exp( -(x.*x/200) ); ``` ```which makes a nice shape ``` ```gf=fft(gt); ``` ``` and for dirac ``` ```dt=zeros(1,101);dt(51)=1; df=fft(dt); ``` ``` then the spectrum of my magical form should be ``` ```rf=df./gf;rt=ifft(rf); ``` ```note rt comes back only real terms as expected inspection looks about right. but I think sum(rt) should equal 1, but is always .03... so that's not right. next remember the step function that was convolved with gt? well, ``` ```out=conv(rt,stept); ``` ```does not recreate the step function at all! ok, well may be it didn't work because time equations should be centered in time so I tried half the gt and half the dirac: interestingly, the df was 1 for all 101 as it should be and the spectrum of fft(gt) looked about right. yet this approach [with much fudging] yielded similar results to the first approach, just different. I think what Im missing is EXACTLY what the effect of the fft over a limited range is doing to things. Need some help here. Robert On Sun, 25 Mar 2007 23:28:15 +0200 Miroslaw Kwasniak wrote: ``` ```On Sat, Mar 24, 2007 at 06:51:53PM -0700, Robert A. Macy wrote: ``` ```Anybody here help with this? I tried using conv and deconv to try something, but it ``` ```does ``` ```not render the expected solution. If I use: ``` ```stept=zeros(1,501);stept(1:251)=1; ``` ```then smooth it with a guassian shaped curve ``` ```smoothedstept=conv(sept,guassiancurve); ``` ```now recover the step by trying to deconvolve doesn't ``` ```work ``` ```out=deconv(smoothedstept,guassiancurve); ``` ```produces an error that the first term is supposed to be non-zero. ``` ```Octave uses deconv(x,w) == filter(x,w,1). This method is algebraically OK but not with real floating point computing :( Errors are in both stages: conv and deconv, but second (inverse filtering) suffers much more when filter is unstable -> roots of the window are outside an unit circle. As I see in case of an gaussian window w=gausswin(n,a) the value rmax=max(abs(roots(w))) is always above 1. When (rmax-1) is below some small value (like 1e-14) the deconv is usable IMHO. For narrow windows you have problems: n=50 a=2 -> (rmax-1) = 1.3323e-15 a=3 -> (rmax-1) = 0.13907 For some cases I did with succes the deconvolution with this recipe (deconvolution via FFT with setting values of an window no less then E and padding to the length of signal): E=.1e-4; A=3; % tuning parameters - as I see min(w1) should be above 1e-22 lw=length(w); lx=length(xs); w1=linspace(0,max(min(w),E),lx-lw+2); w1=[ max(E,w'), w1(end-1:-1:2).^A ]; xd=real(ifft(fft(xs)./fft(w1)))); You can deconvolute in LS sense with: xd=pinv(convmtx(w,length(xs)))*xs; this gives good results but CPU time and memory are eaten. About other methods you can read in E. Pantin, J.-L. Starck, and F. Murtagh, "Deconvolution and Blind Deconvolution in Astronomy" http://jstarck.free.fr/Blind07.pdf ``` ```_______________________________________________ Help-octave mailing list address@hidden https://www.cae.wisc.edu/mailman/listinfo/help-octave ``` ```-- *********************************************************************** Fredrik Lingvall Postdoctoral Research Fellow address@hidden Phone: +47 22 84 00 68 Fax: +47 22 85 24 01 Department of Informatics Group for Digital Signal Processing and Image Analysis Gaustadalléen 23 P.O.Box 1080 Blindern NO-0316 Oslo Norway *********************************************************************** ```