Lens, sensor, and system resolution - a mini-tutorial

Doug Kerr

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As camera sensors evolve, we understandably run into discussions about how the resolution capability of sensors "matches" the resolution capability of typical lenses. As this occurs, however, we often run into two misconceptions which can warp the conclusions people reach:

1. If we have a sensor whose resolution is, say, 50 line pairs per millimeter (50 lp/mm), then if we have a lens with a resolution of 50 lp/mm, that will allow us to take full advantage of the sensor resolution (or sometimes it is said the other way up). But this is not quite how it works.

2. If we have a sensor whose pixel pitch is 100 pixels per millimeter (px/mm), then its resolution capability is 50 lp/mm. But this is not quite how that works, either.

Let me give a brief clarification of both of these matters.

*************

1. Lens, sensor, and system resolution.

When we say that a lens has a resolution of x lp/mm, and are being "scientific" about it, we usually mean that at a spatial frequency of x lp/mm, the modulation transfer function (MTF) of the lens has dropped to some arbitrary fraction of the MTF at "low" spatial frequencies. There is not a consistent fraction always used as this criterion. Let's assume for the moment that we use 0.2 - that is, the spatial frequency at which the MTF drops to 0.2 times its value at a "low" spatial frequency (for some particular spot on the image, perhaps the center) is considered to be the "limit of usable resolution".

Can we see this from the MTF curves published by the lens manufacturer? No. The type of MTF curve they usually publish shows the MTF at different distances from the center of the image but for only two spatial frequencies, one "low" and one "fairly high". So we can't really see how the MTF varies with frequency.

Now, we may wish to reckon sensor resolution the same way. That is, the spatial frequency at which the response of the sensor (its MTF) has dropped to 0.2 its value at "low" spatial frequency is considered to be the resolution capability of the sensor.

Now suppose we have the tidy-sounding situation in which (a) the lens has a resolution of 50 lp/mm and (b) the sensor has a resolution of 50 lp/mm (to just pick an arbitrary number). Great! Now our whole system (camera) will have a resolution of 50 lp/mm, right? Neither the lens nor the sensor is hobbling the achievement of the other, right? No.

We must consider the fact that, in this situation, the MTF of the entire "system" (that is the "chain" through the lens and the sensor) at any frequency is essentially the product of the MTFs of the two components.

In fact, if at a spatial frequency of 50 lp/mm, the MTF of the lens is 0.2 its value at "low" spatial frequencies, and the MTF of the sensor is 0.2 its value at "low" spatial frequencies, then the MTF of the whole chain is only 0.04 (0.2 X 0.2) its value at low spatial frequencies , a response that falls far short of our criterion for the limit of usable resolution for the entire "chain".

So at what spatial frequency does the whole chain exhibit the MTF we consider to be the limit of usable resolution?

Well, let's just suppose that at a spatial frequency of 35 lp/mm:
  • The lens has an MTF of 0.45 its value at low spatial frequency, and
  • The sensor has an MTF of 0.45 its value at low spatial frequency.
[It wouldn't necessarily come out that way.]

Then the MTF of the entire chain would be about 0.2 (0.45 x 0.45) its value at low spatial frequency. Thus we would consider the resolution limit of the entire chain to be at this spatial frequency, 35 lp/mm, not at 50 lp/mm.

Can we figure this out from the MTF curves of the lens and the sensor? No. The MTF curve of the lens does not show us how the MTF varies with spatial frequency (except at two arbitrary frequencies), and we don't usually receive MTF curves for the sensor.

*************

2. Resolution of the sensor

We have a tendency to think that if our sensor has 2400 pixel rows across its height that its resolution is 1200 line pairs in the vertical direction. And if the sensor height were 15 mm, we would equate that to a resolution of 80 lp/mm. But that is not so.

Consider a test target with horizontal lines, alternating black and white, imaged such that on the sensor there were 80 lines per mm. Will the sensor capture its image? Well, if its image happens to fall such that each line were centered on a row of pixel detectors, yes. If it happened to fall so that its lines fell on the boundaries between rows of pixel detectors, no.

Of course, in real life, we are not usually concerned with images that are a series of equally spaced black and white lines. But this concept still applies.

Year ago, in connection with the development of facsimile transmission, Kell investigated this situation, and determined that, averaged over different type of material, where the orientation and alignment of the subject varied randomly, the actual usable resolution of the system would on the average typically be about 75% the resolution implied by the scanning line pitch (which plays the same role as the pixel pitch does in our digital cameras).

Thus, our camera with 2400 rows of pixels across the height of the sensor might in fact develop an actual resolution of 1800 lines per picture height (900 lp/ph).

*************

We find both of these issues at work when we examine the resolution figures reported by, for example, DPR in their camera reviews. For the EOS 20D, for example, DPR reports a resolution in the vertical direction of 1650 lines per picture height (54.6 lp/mm). The "geometric" resolution (that is, based on pixel pitch) is 2336 lines per picture height (77.3 lp/mm). The actual resolution is about 70% the "geometric" resolution, reflecting the impact of the Kell effect as well as the influence of the lens used for the testing.

Best regards,

Doug
 
Year ago, in connection with the development of facsimile
transmission, Kell investigated this situation, and determined
that, averaged over different type of material, where the
orientation and alignment of the subject varied randomly, the
actual usable resolution of the system would on the average
typically be about 75% the resolution implied by the scanning line
pitch (which plays the same role as the pixel pitch does in our
digital cameras).
Sounds like a (-3dB) point to use a crude analogy... excellent information, Doug. Thanks!

--
Bharath
 
Hi, Bharath,
Sounds like a (-3dB) point to use a crude analogy... excellent
information, Doug. Thanks!
It is exactly the same thing we get when we consider the "cutoff" of two cascaded system blocks, using a 3 dB criterion. I would have mentioned that, but I ran out of character count!

Best regards,

Doug

Visit The Pumpkin, a library of my technical articles on photography, optics, and other topics:

http://doug.kerr.home.att.net/pumpkin

'Make everything as simple as possible, but no simpler.'
 
It is exactly the same thing we get when we consider the "cutoff"
of two cascaded system blocks, using a 3 dB criterion. I would have
mentioned that, but I ran out of character count!
Doug,

I guess when you mentioned Kell's investigation, you were not exactly referring to the cascade of the lens and the sensor but the sensor alone -- i.e., alignment of lines with the center of the pixel. This alone yields 70% of sensor lp/mm as the effective sensor resolution? And on top of this, you have the cascade of the lens and sensor giving you an effective lp/mm for the whole system? And, I guess a rough measure for the cascaded system will be 1/(system resolution) = sqrt( (1/lens resolution) 2 +(1/sensor resolution) 2) ?
'Make everything as simple as possible, but no simpler.'
--
Bharath
 
Kell's results concerned the system resolution. Effective resolution of sensor is some number less than 100% of the geometric lp/mm -- probably something like 1/sqrt(1+0.5*0.5) or 90% of the geometric lp/mm.
It is exactly the same thing we get when we consider the "cutoff"
of two cascaded system blocks, using a 3 dB criterion. I would have
mentioned that, but I ran out of character count!
Doug,
I guess when you mentioned Kell's investigation, you were not
exactly referring to the cascade of the lens and the sensor but the
sensor alone -- i.e., alignment of lines with the center of the
pixel. This alone yields 70% of sensor lp/mm as the effective
sensor resolution? And on top of this, you have the cascade of the
lens and sensor giving you an effective lp/mm for the whole system?
And, I guess a rough measure for the cascaded system will be
1/(system resolution) = sqrt( (1/lens resolution) 2 +(1/sensor
resolution) 2) ?
'Make everything as simple as possible, but no simpler.'
--
Bharath
--
Bharath
 
Hi, Bharath,
Doug,
I guess when you mentioned Kell's investigation, you were not
exactly referring to the cascade of the lens and the sensor but the
sensor alone -- i.e., alignment of lines with the center of the
pixel.
Yes - note that I mentioned this in the setcion on sensor reolution.
This alone yields 70% of sensor lp/mm as the effective
sensor resolution?
Yes, and that is a typical "Kell factor".
And on top of this, you have the cascade of the
lens and sensor giving you an effective lp/mm for the whole system?
Yes, indeed.
And, I guess a rough measure for the cascaded system will be
1/(system resolution) = sqrt( (1/lens resolution) 2 +(1/sensor
resolution) 2) ?
What is often cited is:

1/(sys res) = (sqrt(1/lens res) + sqrt (1/(sensor res)) 2

but in fact that depends on the shape of the MTF vs. frequency curves. I forget right now the conditions for which it is so, but it will come to me in a little bit and I'll fill you in! (I'm an old guy, and some of this stuff goes "underground"!)

Best regards,

Doug
 
And, I guess a rough measure for the cascaded system will be
1/(system resolution) = sqrt( (1/lens resolution) 2 +(1/sensor
resolution) 2) ?
What is often cited is:

1/(sys res) = (sqrt(1/lens res) + sqrt (1/(sensor res)) 2

but in fact that depends on the shape of the MTF vs. frequency
curves. I forget right now the conditions for which it is so, but
it will come to me in a little bit and I'll fill you in! (I'm an
old guy, and some of this stuff goes "underground"!)
The expression I wrote assumes Gaussian profile and would be valid wherein the law of large numbers hold. Basically,
(1/(sys res)) 2 = sum of individual (1/resolution) 2

Quite likely, the expression you put down is more appropriate for a cascade of two first-order low pass functions.

--
Bharath
 
Hi, Bharath,
And, I guess a rough measure for the cascaded system will be
1/(system resolution) = sqrt( (1/lens resolution) 2 +(1/sensor
resolution) 2) ?
What is often cited is:

1/(sys res) = (sqrt(1/lens res) + sqrt (1/(sensor res)) 2

but in fact that depends on the shape of the MTF vs. frequency
curves. I forget right now the conditions for which it is so, but
it will come to me in a little bit and I'll fill you in! (I'm an
old guy, and some of this stuff goes "underground"!)
The expression I wrote assumes Gaussian profile and would be valid
wherein the law of large numbers hold. Basically,
(1/(sys res)) 2 = sum of individual (1/resolution) 2
Indeed. I had misread the parentheses in your expression!

Best regards,

Doug
 
Hi, Bharath,

I had forgotten that is the two spreading functions (lens and sensor) were both Gaussian (not an unlikley approximation of many real-life situations), and if we adopt a resolution deinition in terms of the sigma of the spreading function (see below), then we in fact get the relationship you mention for the system resolution:

(1/sys res)^2 = (1/lens res)^2 + (1/sensor res)^2

(This is because the two "spreads" add, thus their variances add.)

Since the Fourier transform of a Gaussian function is another Gaussian function, then the MTF plot for a Gaussian spreading function is also a Gaussian curve, a resolution limit we adopt in terms of a certain MTF value can be related to the sigma of the function, and thus the situation I mentioned above will obtain.

Thanks for helping me to remember this!

If I had been doing this with electrical spectra or measurement errors it would have come to me more clearly!

Best regards,

Doug
 
I think this shows that the resolution numbers from Phil's results (or anybody's for that matter) have some limitations based on the subjectivity of the resolution test. Firstly, for Gaussian profiles, we can derive the relationship (based on variances)

(dx)(MTF resolution in lp/mm) = 1/sqrt(2)

where dx is the pixel pair dimension in mm.

or, effective resolution of sensor is 70% of the native sensor resolution based on pixel size or 55 lp/mm.

This would be presumably the "Kell factor" at play, and the resolution actually measured will vary based on how the lines on the target chart line up with the pixels.

This would be degraded by the MTF of the lens used -- in this case the 50mm prime. Assuming a maximum lens resolution of 125lp/mm, we should see effectively 90% of the effective sensor resolution or 0.9*0.7 = 0.63 of the native sensor resolution.

Hence, we can only describe the measured sensor+lens system resolution with some tolerance for variations (Michael Reichmann in his article on MTF says experts have told him this could even be + - 30%).
Hi, Bharath,

I had forgotten that is the two spreading functions (lens and
sensor) were both Gaussian (not an unlikley approximation of many
real-life situations), and if we adopt a resolution deinition in
terms of the sigma of the spreading function (see below), then we
in fact get the relationship you mention for the system resolution:

(1/sys res)^2 = (1/lens res)^2 + (1/sensor res)^2

(This is because the two "spreads" add, thus their variances add.)

Since the Fourier transform of a Gaussian function is another
Gaussian function, then the MTF plot for a Gaussian spreading
function is also a Gaussian curve, a resolution limit we adopt in
terms of a certain MTF value can be related to the sigma of the
function, and thus the situation I mentioned above will obtain.

Thanks for helping me to remember this!

If I had been doing this with electrical spectra or measurement
errors it would have come to me more clearly!

Best regards,

Doug
--
Bharath
 
Hi, Bharath,
I think this shows that the resolution numbers from Phil's results
(or anybody's for that matter) have some limitations based on the
subjectivity of the resolution test.
Yes, indeed. He does not,. for erxample, use an MTF-based criterion for resolution, about the only one that is truly quantitative.
Firstly, for Gaussian
profiles, we can derive the relationship (based on variances)

(dx)(MTF resolution in lp/mm) = 1/sqrt(2)

where dx is the pixel pair dimension in mm.

or, effective resolution of sensor is 70% of the native sensor
resolution based on pixel size or 55 lp/mm.

This would be presumably the "Kell factor" at play, and the
resolution actually measured will vary based on how the lines on
the target chart line up with the pixels.
Yes, I'm unclear as to exactly the Kell effect relates to an MTF-based outlook on resolution. I need to do some more research on that.
This would be degraded by the MTF of the lens used -- in this case
the 50mm prime. Assuming a maximum lens resolution of 125lp/mm, we
should see effectively 90% of the effective sensor resolution or
0.9*0.7 = 0.63 of the native sensor resolution.

Hence, we can only describe the measured sensor+lens system
resolution with some tolerance for variations (Michael Reichmann in
his article on MTF says experts have told him this could even be
+ - 30%).
I'm not even sure why the DPR results give different reolutions in the horizontal and vertical directions. I have to assume that this may be due to the contribution of the demodasicing process.

It may also be that the photodetectors (more precisely, their "acceptance" areas, taking into account the behavior of the microlenses) are not "square". That certainly has an effect on the actual spread function (not only their pitch), just as the width of the sampling gate interval affects the spectrum of a sampled electrical waveform.

Best regards,

Doug

Visit The Pumpkin, a library of my technical articles on photography, optics, and other topics:

http://doug.kerr.home.att.net/pumpkin

'Make everything as simple as possible, but no simpler.'
 

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