We received a request from an optics pro to add MTF and OTF to our online layman's glossary. These are notoriously difficult concepts to simply explain! So, we added a small entry to the glossary and a longer beginner's definition right here. Please leave your comments if this works for you or if something is still unclear. The glossary and these articles are living documents and we're always open to improving them!

Aka “Modulation Transfer Function”

TLDR: MTF is a term describing how well an imaging system can reproduce contrast as details in the image get smaller and smaller (as far as you, the layman, is concerned). It’s part of a more comprehensive description of imaging quality called “OTF” (optical transfer function) -- see below. In a chart of MTF, typically, the higher the line rides on the Y-axis, the better the MTF.

The longer story:
When an imaging system, like a camera, creates an image of, say black and white lines, as the lines get closer together, the image gets lousier. It’s that way with all imaging systems, even your eyeballs. When you have big lines of black, the black is very black. When you have a fat band of white next to that, the white is very white. 

But when those lines get very thin and crammed together, the whites in an image become more gray and the blacks become more gray, too. And different shades of gray are much harder to distinguish than black vs. white. Also, the edges of where white ends and black begins become harder to pick out, too.
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So if this is the test target we’re trying to make an image of (aka the object):

​The image we create of it might look like some kind of hot mess like this:

How fast an image goes to pot as you get thinner and thinner lines varies between imaging systems. So it’s useful to have a measure of how this differs between different lenses or cameras or telescopes or microscopes. That’s where measuring an imaging system’s MTF comes in (or that of a component of the system).

Let’s break down the words in “MTF” - Modulation Transfer Function.

MODULATION: for the sake of these example tests, this is the change as we go from black to white across the test pattern.

TRANSFER: refers to going from an object in the real world to the image your camera (or lens or whatever) creates.

FUNCTION: meaning you can bet your butt there’s some sort of graph involved. And graphs come into play whenever one result changes in a predictable way as other factors change.

There are different ways to measure MTF along with lots of experts arguing about the best methods. For this explanation, we’re using those simple blocks of black and white. It’s good enough for most rough applications. (If you need something better, you probably already know what MTF is and don’t need to be reading this in the first place.)

So, to come up with a number to represent the contrast we’re seeing in an image, we’ll assign number values to blackest black possible and whitest white possible. Let’s say black = 0 and white = 1. Then, all the shades of gray we’d see in an image will fall between 0 and 1.

Let’s go back to that first example of the fat black and white test pattern. And let’s say the image we got of this reproduced contrast perfectly -- black is all the way black and white is all the way white. So for the black we see here, it will have a value of 0 and white will have a value of 1.

​And let’s also say that in this example pattern below, the test target is 1 millimeter wide. Since there are 2 pairs of black-and-white blocks in this 1 mm-wide pattern, we’ll say this target is 2 line pairs per millimeter or “2 LP/mm”.

Now to get contrast, which is really just figuring out how far apart our different our black and white (or gray) values are, we use this equation:

(Highest Value - Lowest Value) / (Highest Value + Lowest Value)

So it’s the difference between our black and white boxes divided by their total. The highest value is our white which equals 1. The lowest value is our black which equals 0. Filling out the equation, we get:

(1-0) / (1+0)

This reduces down to 1/1, which equals 1. So here, we measured the MTF value at 2 LP/mm to be a perfect 1. (Sometimes this would be expressed as a percentage instead, and in that case, we would get 100%.) And that would be just 1 data point for our MTF curve.

Now let’s calculate the contrast we’re seeing in the second example of black bars that had more line pairs per millimeter.

This was the original test pattern we created an image of:

Let’s say that test pattern was also 1 mm long. This time, there are 5 pairs of black and white we can count, so this contrast measurement will be for the resolution of 5 LP/mm. Now let’s look at that image again.

The black here is not a solid black. It’s gray. On a scale of 0-1, let’s say we measure the blackest part to be 0.2.

The white is also gray, but not as gray as the black appears. On a scale of 0-1, let’s say we found the white to be 0.7.

So now let’s crunch those numbers again in the same formula: (max - min) / (max + min)

(0.7-0.2) / (0.7 + 0.2)

This reduces to: 0.5 / 0.9, which equals: 0.55, rounding to 0.6 (with sig figs). So, our second data point for this MTF curve would be 0.6 on the Y-axis for an X-axis value of 5 LP/mm.

Now let’s pretend that we repeated this for a bunch of other test patterns going up to 10 LP/mm in resolution. The resulting example MTF curve of all those data points might look like this:

As we go to the right on the X-axis, we squeeze more and more line pairs into that millimeter of target area. We can see as we do this, the contrast shown on the Y-axis falls. The black and white bars get harder to distinguish the smaller they get.

If we measured these same targets with another lens and got a line that hovered above the green line we created in the graph, that would be a better MTF curve. The closer the curve gets to that top value of 1 (or 100% depending on the graph), the more contrast we can see in the image.

Keep in mind when you’re measuring MTF a lot of components can bring the contrast down: apertures, lenses and the imaging sensor you’re using can all contribute to degradation. So, if you’re measuring it on your own, you’re probably finding the combined MTF of an optical system, not the individual MTF of a lens.

Also make sure to pay careful attention to the axis labels when you’re reading an MTF chart. Often, lens manufacturers will not have the X-axis represent changing lines per millimeter. Instead, on the X-axis they may show how far from the center of your lens or imaging system or whatever that a measurement was taken.

Usually at the very center of a lens, light rays aren’t getting bent a whole lot. That means the lens doesn’t do a lot of work in the center. And if it’s not doing a lot of work, it’s tough to tell if it’s messing anything up. But as you move away from the center of a lens or imaging system, the light rays that hit there are bent much more, and therefore have a chance to get screwed up more. Usually, you’ll see MTF drop off as you take measurements farther from center.

If a lens manufacturer puts “distance from center” on the X-axis, usually the line they’re graphing will be a bunch of measurements of the same “line pairs per millimeter” chart placed at different positions in the image. So you may have one line representing a bunch of measurements made with a 10 line pairs per millimeter (10 lp/mm) chart and another line representing 30 lp/mm.
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One more thing: the orientation of your test pattern also matters! Sometimes you’ll see the MTF lines labelled as “sagittal” or “meridional”. That just means the test pattern they measured with was rotated about 45 degrees clockwise or counterclockwise, respectively. (The more precise definition is it’s actually rotated so the test pattern lines line up with the diagonal of the sensor chip for sagittal measurements. Then the meridional rotation is 90 degrees counterclockwise from the sagittal one. Because imaging chips aren’t usually square, those angles are not actually 45 degrees . . . but thinking of 45 degrees to the right or left is good enough for visualization purposes.)

Aka “Optical Transfer Function”

OTF describes how well an imaging system creates an image. Part of OTF includes MTF, dealing with contrast and resolution (see MTF definition above). MTF answers the question of: how black is black and how white is white in small details on an image? Another part of OTF deals with how an imaging system messes with phase in the “PTF” or phase transfer function.

So, OTF is all of MTF and a bag of phase chips.

The more precise definition has to do with the equation for OTF being a complex function. Remember those? No, it’s not that the function is very difficult (well that’s not what it’s supposed to mean, at least). It’s that it involves the imaginary number “i” -- that funny letter that means the square root of -1.

If the way a system creates an image can be described with an OTF that has all positive values with none of that “i” nonsense in it, then the chart for OTF looks the same as the chart for MTF. BUT: if the OTF chart dips down into negative numbers, then that means you have phase getting messed with, too.

If an imaging system screws up phase you’d see a test pattern of black and white lines like we’ve been looking at shift to the right or left in an image. If that happens, you know there’s an aberration in some component of your system.
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So if this was your original signal pattern (against a green background just so we can clearly see the edges):

​If the image had just its phase messed up and contrast remained the same, it could look something like this:

Instead of the image starting at the beginning of the first line pair like in the test pattern, it starts in the middle of the first white block. So if you saw this last image, you would expect the data point on an OTF chart (that represents this particular line spacing) to be a negative number for the Y-axis.

But the OTF would be able to quantify both the shift in the pattern PLUS the blur in contrast (MTF). So the situation you might see below with both MTF showing decreased contrast, and the phase getting messed up, too, can be completely described:

The data point that represents this last one would still be a negative number, because the phase shifted. But taking the absolute value of that number (meaning you ignore the negative sign) would show you the MTF.

There you go!

This glossary is written simply, with as little confusing jargon as possible, to aid the layman's understanding. If there are more words you need explained here, please drop us a note and we'll add them!

Typically pronounced "abbey" (like where monks live). Also called "V-number".

​This value describes how differently a transparent material will bend different colors of light that pass through it, aka the amount of dispersion. High Abbe numbers represent materials that have low dispersion. Lower Abbe numbers represent higher dispersion, or materials that bend red light a lot differently than than they bend blue light (and all the colors in between).

So, if you want to make a prism that separates white light into the many colors it's made of, you're better off creating that prism with a low Abbe number material. Usually, though, high dispersion is not a desirable characteristic when designing an optical system - especially in imaging optics. If Abbe number is an important factor in your system, usually you'd be looking for materials with a high Abbe number, or low dispersion.

BRDF stands for "Bidirectional Reflectance Distribution Function". It is a mathematical description of a surface finish, or rather, how light reflects off a surface.
If a surface is pretty evenly rough, you can usually get a BRDF measurement of it and create an optical model from that. Every optical simulation software has its own method of importing and formatting these models and usually, you'll have to have a custom model made for each optical software being used and for each material. Once you have a working model in your optical software, you can simulate the surface texture in your simulations, which is often super important.

This is a description of light color in units of Kelvin. Lower Kelvin means yellower or more red/orangey color. Higher Kelvin means bluer color. See longer article on CCT HERE.

This term can refer to the surface finish of a material. If it's not specular (see below), it's probably diffuse. Diffuse means a rougher surface than, say, a mirror and a rough surface means that light coming it to hit that surface will encounter lots of different incident angles. So then, when it bounces off the surface, it will be leaving at all sorts of angles.

If you think about a small, red laser beam hitting a specular mirror surface, that beam is still going to be a small dot of light when it leaves. On the other hand, if your small, red laser beam hits a diffuse surface like sand paper, that small red dot is going to spread out into a bigger blob. The light rays going onto the sandpaper would be closely similar, and the light rays leaving the sandpaper would be shooting in all sorts of directions.

TLDR: Color temperature (or CCT: Correlated Color Temperature) is commonly used to describe the color of white light in illumination (lighting) applications. It's measured in Kelvin -- the same way we measure how hot really hot stuff is. White light with a higher Kelvin is more bluish, white light with a lower Kelvin is more yellowish.

"When I buy a light bulb or a lighting fixture or an LED -- what do these friggin' numbers mean?"

27K or 2700 K or 2700 Kelvin - "warmest" or most orangey-yellow standard lighting color. Think Grandma's old school incandescent lighting, on the side of candle light (although not quite that warm).

3K or 3000 K or 3000 Kelvin - still on the warm side, but a bit of a brighter-feeling, bluer white than 27K.

35K or 3500 K or 3500 Kelvin - a common color of indoor white lighting -- bluer than 3K, but not as grating on the nerves as 4K.

4K or 4000 K or 4000 Kelvin - the "bluest" or "coolest" typical white lighting color. You might find this in clinical settings or where retail stores want to make shiny baubles look extra sparkly.

It's weird, right?

​It is. It's OK to think it's weird.

There is a reason behind the madness, though!

The color of light described in Kelvin is the color of a thing called a "black body radiator". As its burning temperature changes, its color changes. The hotter it burns, the bluer the light. Just like your parents may have told you if they loved you: blue and white flames are hotter and more dangerous to little kid fingers than orange and yellow flames.

A black body radiator is not a real, physical thing, however. It's a theoretical construct. There aren't real black body radiators that we know of in real life (yet?). The idea is of a material/object that doesn't reflect any light whatsoever. No light can bounce off it. The only light we see from this object are the wavelengths it radiates or burns.

So, it's similar to a lump of coal. If I asked you what color the coal was it would depend on if it was on fire or not! If it were not on fire, you might say it was black. If the coal was burning, you might say orange. If you were to measure the temperature of the orange bits of burning coal, you could describe that specific color orange with the temperature you measured.

"This color orange is the hue of char-your-flesh-hotness Kelvin."

This visualization works OK except for when that coal turns to ash as it's burning. At that point, it's got some white and gray ash in there that will reflect outside light that hits it. Then what color is it? It's all sorts of colors at that point! Which is why to describe these colors, scientists made up a pretend thing - the black body radiator - that reflects no light at all ever. Not before it's "lit", not as it burns -- it never reflects any light. That way, there is a consistent understanding of how to describe "red hot" and "white hot" colors of light and everything in between.

Drop us a note with your optics question, and we'll try to get it answered on this site.