BTW, This is a very nice explanation. Thanks. On Sep 29, 10:34 am, kfj <_...@yahoo.com> wrote: > Let me explain: enfuse looks at all pixels in all images in turn. For > every pixel, it calculates it's 'quality' by a certain criterion. When > taking well-exposedness as criterion, how can well-exposedness be > defined? Enfuse's approach is to look how 'near' the pixel is to a > 'good' exposure. --exposure-mu defines 'good' and --exposure-sigma > defines 'near'. The default value of exposure-mu of 0.5 means that per > default pixels which are precisely between black (0) and white(1) are > considered 'good'. The 'near' parameter is more difficult to grasp - > it's the standard deviation from the mean defined with exposure mu, > hence the sigma. The default of 0.25 here has the effect that roughly > the middle half of brightness values will be considered better than > the rest, and the closer to mu the better - it's a gaussian bell > curve. If you want to fool enfuse into considering all pixels equally > good, you can take a large value for sigma and get a bell curve which > is so flat it's almost plane. That's where the sigma of 50 comes in - > a value you'd never use for 'real' exposure fusion but which should > work for your specific case. > > After all images in all pixels have been given a quality measure, > enfuse combines them, weighting each according to it's quality. If > they all have the same 'quality' enfuse will simply produce an average > of all inputs, sice it considers them all equally good. > > Kay
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