On Jul 26, 12:34 am, ti_chris <tiChri...@[EMAIL PROTECTED]
> wrote:
> On Jul 24, 7:11 pm, DharmaFog <Agile.Asp...@[EMAIL PROTECTED]
> wrote:
>
>
>
> > On Jul 22, 12:18 pm, "Fredo" <fr...@[EMAIL PROTECTED]
> wrote:
>
> > > I have an image that I need to normalize the brightness on. The
image is
> > > greyscale 8-bit. I'm told I need to do a best fit linear filter. How
do I do
> > > this? Is this just a linear regression? If so, I need X&Y values and
I'm not
> > > sure what should be X vs. what should be Y. I assume one of them is
the
> > > pixel brightness. What's the other?
>
> > > Thanks
>
> > It's actually an exercise in simple ratios.
>
> > First find the maximum and minimum gray scale for the
> > image you want to normalize, i.e., the range of the existing
> > values.
>
> > Suppose you want to normalize the values to N=255.
>
> > Then
>
> > new_value=(old_value-minimum)*N/(maximum-minimum)
>
> Although this can generate decent results, it is definitely not
> ideal. Take the example of an image that has a concentration of
> middle-range pixels and one or two pixels at the boundaries. In such
> a case, you would not scale the image because min = 0, max = 255,
> while in reality, you would benefit from over-saturating those couple
> pixels that are out of range. Fredo is right. You need to apply a
> best-fit to achieve good results here.
>
> I'm guessing you're really looking to do a linear regression. You can
> find lots of resources if you google for that. A simple one is
> wikipedia:
>
> http://en.wikipedia.org/wiki/Linear_regression
>
> You're effectively looking at applying least square on the difference
> between the "new value" and the "old value" given a line y = mx + b.
> Least square should minimize things decently enough that it should
> look much better than what DharmaFog is proposing. If you want a
> quick and dirty thing that works however, his suggestion is quite
> valid.
First, normalization implies scaling - it's doesn't imply molesting
the histogram distribution.
Second, I really don't know what a "best fit linear filter"
is since "best" hasn't been defined.
And if was defined, then I'd probably filter the image using
a FIR filter.
Third, in your particular case - where you evidently want
to modify the existing histogram distribution - I would
recommend trying histogram equalization
http://en.wikipedia.org/wiki/Histogram_equalization
But beware, when you use histogram equalization (or any
other method which molests the distribution) you run the risk
of increasing the noise and decreasing the signal.
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