In this course, you will learn the science behind how digital images and video are made, altered, stored, and used. We will look at the vast world of digital imaging, from how computers and digital cameras form images to how digital special effects are used in Hollywood movies to how the Mars Rover was able to send photographs across millions of miles of space. The course starts by looking at how the human visual system works and then teaches you about the engineering, mathematics, and computer science that makes digital images work. You will learn the basic algorithms used for adjusting images, explore JPEG and MPEG standards for encoding and compressing video images, and go on to learn about image segmentation, noise removal and filtering. Finally, we will end with image processing techniques used in medicine. This course consists of 7 basic modules and 2 bonus (non-graded) modules. There are optional MATLAB exercises; learners will have access to MATLAB Online for the course duration. Each module is independent, so you can follow your interests.
3 - Histogram equalization - Duration 19:56 - Optional breaks at 04:40 and 11:30
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Из курса от партнера Duke University
Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital
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Duke University
480 оценки
Из урока
Spatial processing
Some of the most basic tools in image processing, like median filtering and histogram equalization, are still among the most powerful. We will describe these and provide a modern interpretation of these basic tools. Students will then become familiar with simple and still popular approaches. We will also include non-local means, a more modern technique that still uses classical tools.
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Guillermo SapiroProfessor
Electrical and Computer Engineering
Hello and welcome back to our image and video processing class.
In the previous video, we saw in real time, histogram equalization.
We saw how it improves the image and how we managed to get a non-uniform histogram
and make it a uniform histogram. So, let's see in detail how we do that.
I'm going to show you exactly the math behind that and you will see how simple
it is. You would be able to go right away and
program that in your own computer if you wish to do so.
So, let's just dive into it. So, first of all, let's just go again and
talk about histograms. Here we see a very dark image and here we
see the histogram. As we see most of the distribution of the
pixels value, of the pixel values are in the low end of the histogram, there's not
a lot of bright values and we see that the histogram basically, they are, the
count of pixels there is very, very low. Here, we see the other way around, a very
bright image and most of the count is in the high part of the histogram.
So, there is not a lot of pixels with low values.
Here, we see kind of a great low contrast image and then we see that the pixels are
actually concentrated in the middle, while here, we see an image that looks
much nicer. These are all the same images, this looks
much nicer and we have kind of a uniform distribution.
We're occupying the whole space of pixel values, more or less uniformly, and then
the image looks much, much nicer. So, we want to be able to learn how to go
from a distribution like this to a distribution like this.
And again, as I say, it's a very, very simple operation, as we're going to see
in just a minute. So, our goal is to draw form a given
distribution to a uniform distribution assuming that our pixel values can go
from zero to L minus 1. For example, standard L is 256, so we can
go from zero dark to 255 very bright or white.
And we want to get a uniform distribution.
So, basically, the probability or if we don't normalize the count,
but let's just think normalize is just easier,
the probability is one over L minus 1. That's basically the probability of
having a pixel achieve that gray value. So, we want to go from this to this.
That's basically our goal. In order to achieve that, what we needs
to find a transform that takes basically the pixel values of the original image
and transforms them to pixel values of a different image.
So, having an image and you'd basically to decide, for every pixel in the image,
I want to see its value and I want to transform it to exactly the same place
but with a new value. And remember, so far, we are only making
decisions based on the pixel value. Not on the neighborhood,
just on the values. If the value is seven, we need to decid
what we transform seven to regardless of what are the pixel values around it.
So, we need a transform that takes us from any pixel value to a new pixel value
in such a way that we get a uniform distribution.
There is going to be one requirement of this transform and we are going to make
an increase in transform and the reason is we don't want to reverse the pixel
values. So, we want this to be a monotonic
increasing transform. If seven goes to ten, eight should go to
a value at least ten, may be above ten. But we don't want it to go below ten and
then we actually kind of flip the content of the image and that's not the intention
of histogram equalization. So, we are going to have increase in
functions and finding these functions is our goal.
Now, sometimes we have strictly monotonic increasing functions that every pixel
values goes to a different pixel value. Sometimes, we have an increasing function
that is monotonic that some of the pixel value might end up with the same target.
And we would see that that will happen in histogram equalization.
In particular, when we work with these great images and its great pixel values.
So, we are going to end up with a transform of this style and not to
transform of this style. That's basically a problem with histogram
equalization in this [UNKNOWN] computers, something that we must have, we cannot
avoid, as we're going to see in the next. So, how do we achieve this?
The basic idea is very simple. We can think about an histogram as
basically a distribution s. So, this is where you need a little bit
of background in probability. But don't worry because even if you don't
have that background, I think it's going to be almost self ex, self-explanatory,
what I show next. So, we have a distribution.
The histogram is nothing else than a distribution.
What's the probability of a given pixel value?
We have, we now want to obtain a new one. We actually have the current one.
So basically, we have a new, a different distribution.
And remember, what we are looking is for a map from r to s.
We are looking for this transform. So, it turns out, this is a basic result
of probability that if we have such a map, then the relationship between these,
to this division, is as follows. This is the relationship.
So, the new probability distribution is the old probability distribution times
the absolute value of these derivative. That's a well-known result from
probability distribution and it's what you need to understand the rest of the
derivation. So, let us assume, for a second, and I'm
going to prove you that this is the transform the we're looking for.
Let's assume for a second that this is my transform.
So, s is a transform. So, from any pixel value, I will get a
new pixel value. And let's write it as L minus one times
the integral from zero to r. And I'm going to explain the actual
interpretation of what I'm writing in a second of the current histogram or
current probability distribution, P(w)dw.
So, what am I doing here? I'm basically integrating the mass, the
number of pixels that achieve a given gray value from zero to r.
And then, I'm doing this because I'm using probability functions, so I'm
normalized between zero and one, and I want to get back gray values.
So, let's look again at the transform. You count, if we are in a discrete space,
your going to count, if you want to know to which pixel value should I transform
the value r equals seven, you count how many pixels are at zero,
how many at one, at two, and three, and so on until seven and that gives you the
transform. I have to prove to you that this is the
right transform to do histogram equalization.
So, let us do that. In order to do that, I'm going to compute
this derivative. And the basic idea is very simple.
So, we are going to actually do ds/dr. Now, we have the explicit transform here,
so we can compute this derivative. It's basically d of T(r)/dr and we have
here the expression, so it's the derivative according to r of this
expression that we have here, L minus 1 times the integral from zero to
r, Pr(w)dw. I apologize that the lines, that
rows are going one on top of the other, but I think it's very clear.
So, we need to compute this derivative and if you remember basic Calculus, this
is a constant so it will go out, okay?
And then, the derivative of this integral is what's inside the integral.
So, this is equal to L minus one times and I'm going to write this here in the
next line, Pr(r).
So, we obtain that the derivative of s according to r is L minus one times the
probability, basically, the original histogram.
Now, what we are going to do, I'm going to erase this in a second but
what we are going to do is basically include this here and see what happens.
So, I'm going to plug this in here but look what we have here, we have dr/ds and
I have here. ds/dr.
S,o we need to invert that, it would be one over this.
And I hope that you can see what's going to happen, so let's see actually what's
going to happen. Again, we have P,
the new probability that we are looking is the old probability,
dr/ds, absolute value. We computed dr/ds just a second ago.
So, this is the Pr(r), absolute value of one over L minus 1, that was our
normalization, Pr(r). Now, probabilities are positive and this
is positive, certainly positive because L is greater than one.
So, I can get rid of the absolute value and then I see that this will basically
cancel with this and I've got one over L minus 1 and that's exactly what I wanted.
Exactly what I wanted is to have a uniform, a constant probability
distribution for my new image, okay? So, once again, I'm going to write
again what's actually the, the actual transform is,
which is a map of r is L minus 1, the integral from zero to r of the
original probability distribution of function or histogram.
So, it's a very simple function. You go and you count how many pixels have
a value up to r and that's your transform.
Very, very simple operation. It's basically a one-line code in almost
any programming language. And that will achieve us histogram
equalization. So, let's see this in action.
First, let's just take a, basically, an artificial sample, where we have eight
different values here. This is a counting, so we have 790 pixels
that have value zero. And 1,023 pixels that have value one, and
so on, and we just transformed these into a probability, and because we have been
working with probabilities and this is the probability that we are going to get,
we plug that into the formula that I just showed you, and this is what we're going
to get. So, first of all.
This is the original instagram, Pr. This is the distribution that we have.
If you plug that into the formula which I showed, this is going to be the
transform. Note, it's going to be monotonic.
And we know that because we are integrating.
And let me just write that again. The basic operation was to integrate from
zero to r. So, the more I integrate, because what I
have in here is positive, it's clearly an increasing function.
I'm only adding more and more positive numbers so it has to be positive.
So, it's an increasing function, and what it tells me, it tells me that zero goes
to this number. One goes to this number, and so on.
So, it becomes a very, very simple map. And this is going to be the new
histogram. Now, you may wonder, it looks more
uniform and one of the things that happens because we are working with
discrete data, we're working with images in the computer, we're going to have to
round numbers. So, for example, the 4.2 will become
four. So, we end up actually with less peaks
than we have here because this ones here at the end, will merge into the same
integer number. And that's a price we play, we pay for
working with this great images. We cannot represent 6.7 and 6.8, will
both become, although the formula tells us they should go to different numbers,
they will both become, let's say, seven. In a similar fashion, Let me ask the
following question which I'm going to ask you to think for a second and then
respond to me. Let's just look at an histogram that has
the following form. It has only two different pixel values,
these two. It doesn't matter what exactly are the
values here, but this is the original distribution, of pixel values.
There are only two values in the image. This is a binary image with only two
different colors, two different pixel values.
And I'm going to ask you what's the relationship between the map for a pixel
value here and a pixel value here? So, let's just call this A, let's just
call this B, and I'm asking you the relationship between T(a) and T(b).
Are they equal? Are they different?
Is T(a) greater than T(b)? Is T(a) less than T(b)?
So, just think for a second and give me your answer and I'm going to come back
right away and give you the explanation for the correct answer.
So, let's look at what is the correct answer.
Remember, what we are doing in histogram equalization is we are integrating,
okay? So, I ask you, what's the relationship
between these two, are equal, less, or greater?
We know first of all that T(b) should be greater or equal, than T(a) because we
have a monotonically increase in function. But let's just look a bit more
in detail, what happened. This actually is going to integrate the
whole mass up to here. So, basically its going to integrate only
this. Here, is going to integrate everything up
to b. But there is no new mass between a and b.
Since there is no new mass then what happens is that T(a) would equal to T(b).
And as an extra exercise, you can think about what's going to be actually to
transform for this one and what's going to be the transform for this particular
one. And especially, you can relate it to the
average in the image, just do that as an exercise on the side.
But this is very important illustration that the basically, transform that we're
looking for, even before we have to round or quantize because we're working with
these great images. Even before that, it's not strictly
monotonic, there might be more than one values that end up with the same
transform. You can, of course, say, we don't really
care too much about that, because in the original image there were no pixels with
initial gray value a. And that's correct but I want to show
this both to illustrate that the transform does not have to be
monotonically, strictly monotonically increasing.
It has to be monotonically increasing but not strictly.
And also to illustrate once again that all what the histogram equalization is
doing is basically counting the number of pixels after the value that you want to
mark. So, I'm counting the value, the number of
pixels up to a and then the number of pixels up to b.
So, after this exercise, let's just see few additional examples.
We, of course, saw examples with the online demo, but let's just illustrate
one more example, which I think is very useful.
I'm going back to the images that we started.
This was dark. This is what's happening after histogram
equalization and the histogram looks much nicer than before.
So, let's just look at the map. This is the map.
Pixels value form here can map to here, okay?
So, this is my r and this is my s. And look at the map.
The map is basically is very, it has a very, very high slope because it's trying
to very fast map very dark pixels to brighter pixels.
So, it has a very, very sharp slope. Let's look for example at this one.
This one was pretty good to start from. It hasn't changed a lot and actually this
is the map. It's almost unislope, in mapping the
diagonal, there's almost no changes in the pixel values.
And similar interpretations for the other two maps.
This one actually needed, needs to darken the pixels and we see that in the
corresponding map, that basically, look what happens here.
And this one needed to stretch the pixels.
Remember, they were in the middle, it needs to stretch them and we actually
observe that here. It's stretching the pixels from the
middle. They were all around here to start from
and it's going to stretch them and we see that it actually occupies the whole range
from this which was the very big, very narrow band, that it occupies the
original histogram with very low contrast is actually mapping out to the whole
spectrum from zero to 255 and we see that in the map as well.
And, of course, we end up with images that are very similar, regardless of
where we started because these images actually were created from these images,
from an original, let's say, this one, just by changing the
contrast. And then after we equalize them, they look much closer.
And that's a technique that is used very often to make images that we're taking
under different conditions look much more similar.
We equalize the histogram, at the same time, we're making the images look much
more similar and then we can compare between them in a, in a much more
friendly fashion. So, this is basically instagram
equalization. What we are going to do next in the next
video is instagram transform. What happens if I don't want to equalize
an histogram? I want to transform it to an histogram
that I know is going to be very good for my image.
We are going to see that in the next very short video that is also a very, very
simple operation. Thank you.
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