c# - Normalizing an image after (gaussian) filtering -


i have implemented gaussian filter following algorithm of nixon aguado. algorithm (after finding template described here gaussian template) following.

the pseudo code in matlab style believe.

function convolved=convolve(image,template) %new image point brightness convolution of template image %usage:[new image]=convolve(image,template of point values) %parameters:image-array of points % template-array of weighting coefficients %author: mark s. nixon %get image dimensions [irows,icols]=size(image); %get template dimensions [trows,tcols]=size(template); %set temporary image black temp(1:irows,1:icols)=0;  %half of template rows trhalf=floor(trows/2); %half of template cols tchalf=floor(tcols/2); %then convolve template x=trhalf+1:icols-trhalf %address columns except border     y=tchalf+1:irows-tchalf %address rows except border         sum=0;         iwin=1:trows %address template columns             jwin=1:tcols %address template rows                 sum=sum+image(y+jwin-tchalf-1,x+iwin-trhalf-1)* template(jwin,iwin);             end         end         temp(y,x)=sum;     end end  %finally, normalise image convolved=normalise(temp);

anyway, worries me last part "normalise". have tried algorithm (written in c#) , pixels got value 255.00000003 (which larger 255). should "normalise" results enlarge 0-255 range? wouldn't modifying image (other gaussian) wan't operation involve gaussian filter , nothing else.

edit: have eliminated "normalization" , seems works well, have no idea why authors of book recommended it. still, worries me program crash if reason value > 255 appears , cannot drawn.

as has been pointed out others in comments, normalizing image in sense ensures range of each channel 0 255 bad.

normalizing image in sense each value clamped between 0 , 255 should not necessary appropriate filter kernel. in practise can necessary or useful because of way floating point numbers work. floating point numbers can't represent every possible real number, , every computation can introduce inaccuracy. might cause of 255.00000003 1 of values.

like many signal processing algorithms, 1 assumes discrete time/space, continous values. it's easier reason kind of algortihms , describe them mathematically.

on computer don't have continuous values. images use discrete values, integer between 0 , 255 each channel (8 bit per channel). sound encoded 16 bit per channel.

in vast majority of cases perfeectly acceptable, yet filter (although non-linear one) after gaussian filter output. yes, in strict sense modify output of gaussian filter, either when save image or when display on screen.


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