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The amazingly high resolution of a Helios 44M-4
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PostPosted: Tue Apr 15, 2014 9:06 am    Post subject: Reply with quote
calvin83 wrote:
scsambrook wrote:
I love to see these tests, far beyond my ability to replicate them. The best I can manage is to put up boxes of cereal and dog food and take pictures of them with the camera on a tripod.

Would it make any difference if the test chart had its lines printed in different colours? Would some lenses manage green lines better than blue ones, for instance?

Take a look of this test in dpreview.
http://www.dpreview.com/reviews/sigmadp1/page20.asp

Different lens will responds to different wavelength differently. So, a lens may be better manage green lines while other lens are better in red.


Thanks for that link, Calvin !


PostPosted: Tue Apr 15, 2014 11:49 am    Post subject: Reply with quote
fermy wrote:

For the sake of the argument, let's assume that the noise level is 1%. That corresponds to the level of white lines with noise subtracted of 140-0.01*200=138 and MTF value of 8/150=5.3


You are on the right track, but you should consider that:
1) The noise may be positive or negative, depending on the point of the image.
2) The noise affects both the white and black lines.

The values ​​should be averaged along the white and black lines, not only subtracted from the level of the white line.

Thank you for your interesting remarks! Smile


PostPosted: Tue Apr 15, 2014 8:42 pm    Post subject: Reply with quote
I've had two Helios 44M lenses and neither was particularly notable for any quality. That's why I don't have them anymore. Not that they were bad (well one was not so well made fit and finish wise) but at least here in the U.S. you can pick up a bunch of lenses that perform noticeably better for less money.

Most 50s perform very well in the center, and there is always sample to sample variation present. I think somebody did a comparison on this forum of several Domiplan lenses, and the good ones were very good, while the bad ones were pretty bad. And the Domiplan has a much worse reputation than the Helios.


PostPosted: Wed Apr 16, 2014 12:04 am    Post subject: Reply with quote
Gerald wrote:
fermy wrote:

For the sake of the argument, let's assume that the noise level is 1%. That corresponds to the level of white lines with noise subtracted of 140-0.01*200=138 and MTF value of 8/150=5.3


You are on the right track, but you should consider that:
1) The noise may be positive or negative, depending on the point of the image.
2) The noise affects both the white and black lines.

The values ​​should be averaged along the white and black lines, not only subtracted from the level of the white line.


Well, not really, there is no "negative noise" in photography. Noise can add to the level of signal, but can not subtract from it because photosites are additive devices by their very nature. Furthermore, noise is random, some photosites are affected by it, while others are not. If I understood your methodology correctly, to determine the contrast you sampled the darkest and the brightest values. In that case, the darkest values will have the same level as the darkest values in the absence of noise, while the brightest values will have the noise added. Sure, you can average the values along the darkest and brightest lines and then calculate the difference, that would give you correct contrast values for the additive noise, however I doubt you have done that. Did you?


PostPosted: Wed Apr 16, 2014 1:27 am    Post subject: Reply with quote
fermy wrote:

Well, not really, there is no "negative noise" in photography

Sure there are negative noise in photography!
Noise is a fluctuation around an average. It can be positive or negative. For example, suppose you shoot a perfectly uniform wall and then you count the number of photons captured by each sensor cell. Let's say the numbers are: 958 1032 992 1000 1018 ...

Suppose also that the mean is equal to 1000. The noise of each cell is given by:

958-1000 = -42
1032-1000 = +32
992-1000 = -8
1000-1000 = 0
1018-1000 = +18

The noise of the first cell is negative (-42), the second is positive (+32), and so forth.

If you shoot a new picture, the noise of each cell will be very different because the arrival of photons is a random phenomenon, but the mean will remain the same (1000) if the exposure was not altered.


fermy wrote:

Sure, you can average the values along the darkest and brightest lines and then calculate the difference, that would give you correct contrast values for the additive noise, however I doubt you have done that. Did you?


You always doubt what I do! Laughing
What I did was to move the cursor of Photoshop along the lines and eyeballed the averages. A rigorous procedure would require writing a computer program.


PostPosted: Wed Apr 16, 2014 2:01 am    Post subject: Reply with quote
fermy wrote:






Excuse me, are you a mathematician? Or a physicist? Question Question Question


PostPosted: Wed Apr 16, 2014 2:37 am    Post subject: Reply with quote
Edited

Last edited by bernhardas on Tue May 10, 2016 7:34 am; edited 1 time in total


PostPosted: Wed Apr 16, 2014 1:50 pm    Post subject: Reply with quote
Gerald wrote:

Excuse me, are you a mathematician? Or a physicist? Question Question Question


Mathematician

Coming back to our discussion, noise is not necessarily a fluctuation around average. In photographs noise manifests itself as light spots, never as dark spots, which should tell you that noise' contribution is always positive.


PostPosted: Wed Apr 16, 2014 3:35 pm    Post subject: Reply with quote
Gerald wrote:
fermy wrote:

Good point on the noise, but when you take the noise into account strictly speaking it is not clear how MTF x is defined. Btw, how does 7-10% figure was obtained, is it what Imatest spits up?




I estimated the contrast by using Photoshop to measure the levels. The white level of the image is ~ 200, and the black level is ~ 50 (the black region of the image is not shown in the crop).

The level of the black lines is ~ 130, and the level of white lines is ~ 140.

The contrast is calculated as follows:
Contrast = (140-130) / (200-50) = 7%

These values are approximate due to the noise. Another person could find slightly different values.


It's not extremely significant, but be careful about taking values from photoshop. They are not linear, because gamma is applied in the conversion from raw file to jpeg/tiff. Don't know if MTF is defined on a linear scale though.
If linear scaled photos are needed to measure MTF then software for astrophotography can do this (present RAW files linear)


PostPosted: Wed Apr 16, 2014 8:59 pm    Post subject: Reply with quote
fermy wrote:
Gerald wrote:

Excuse me, are you a mathematician? Or a physicist? Question Question Question


Mathematician

Coming back to our discussion, noise is not necessarily a fluctuation around average. In photographs noise manifests itself as light spots, never as dark spots, which should tell you that noise' contribution is always positive.


I agree with you - noise is positive - but not for the reasons you outline. Have you never seen a picture of a clear blue sky where both light and dark spots can be seen ? These correspond to positive and negative deviations respectively from a mean or expected value. A major source of noise is so called photon shot noise which varies with the square root of the signal. The signal is essentially a photon count so one normally takes the positive square root. I'm not sure what kind of physical situation would be described by a negative signal to noise ratio - for example.

A useful account of Noise in DSLRs is given by Emile Martinec : http://theory.uchicago.edu/~ejm/pix/20d/tests/noise/index.html


PostPosted: Wed Apr 16, 2014 9:41 pm    Post subject: Reply with quote
sichko wrote:
fermy wrote:


Coming back to our discussion, noise is not necessarily a fluctuation around average. In photographs noise manifests itself as light spots, never as dark spots, which should tell you that noise' contribution is always positive.


I agree with you - noise is positive - but not for the reasons you outline. Have you never seen a picture of a clear blue sky where both light and dark spots can be seen ? These correspond to positive and negative deviations respectively from a mean or expected value. A major source of noise is so called photon shot noise which varies with the square root of the signal. The signal is essentially a photon count so one normally takes the positive square root. I'm not sure what kind of physical situation would be described by a negative signal to noise ratio - for example.

A useful account of Noise in DSLRs is given by Emile Martinec : http://theory.uchicago.edu/~ejm/pix/20d/tests/noise/index.html


Well, I am not giving any reasons in the quoted piece, I merely provide the empirical evidence. The reason was given a couple of posts above, it is the fact that sensor is essentially photon counting device, it does not subtract. However, after looking at the link that you've posted, I think my original statement is not entirely accurate either. It appears that read noise, for example, can be negative due to voltage fluctuations in the sensor readout circuitry, which can go either way. So I guess a more precise statement is that the noise is mostly positive.


PostPosted: Thu Apr 17, 2014 11:42 am    Post subject: Reply with quote
sammo wrote:
Gerald wrote:
fermy wrote:

Good point on the noise, but when you take the noise into account strictly speaking it is not clear how MTF x is defined. Btw, how does 7-10% figure was obtained, is it what Imatest spits up?




I estimated the contrast by using Photoshop to measure the levels. The white level of the image is ~ 200, and the black level is ~ 50 (the black region of the image is not shown in the crop).

The level of the black lines is ~ 130, and the level of white lines is ~ 140.

The contrast is calculated as follows:
Contrast = (140-130) / (200-50) = 7%

These values are approximate due to the noise. Another person could find slightly different values.


It's not extremely significant, but be careful about taking values from photoshop. They are not linear, because gamma is applied in the conversion from raw file to jpeg/tiff. Don't know if MTF is defined on a linear scale though.
If linear scaled photos are needed to measure MTF then software for astrophotography can do this (present RAW files linear)


Yes, you're right. Programs like Imatest and similar linearize the data before calculating MTF. That said, the above calculation is for illustration only. In the traditional method, the determination of the limit resolution is based on a purely visual judgment, which does not explicitly use the MTF.


PostPosted: Thu Apr 17, 2014 12:28 pm    Post subject: Reply with quote
sichko wrote:
The signal is essentially a photon count so one normally takes the positive square root. I'm not sure what kind of physical situation would be described by a negative signal to noise ratio - for example.

A useful account of Noise in DSLRs is given by Emile Martinec : http://theory.uchicago.edu/~ejm/pix/20d/tests/noise/index.html

The signal-to-noise ratio, SNR, is defined as:

SNR = (signal power)/(noise power)

SNR is always positive because power is a positive quantity. However, the instantaneous value (or the value at each point, if you're talking about image) of the noise can be negative! This is clear if you read carefully this excerpt from the article you cited, where the author comments on shot noise (highlighted text by me):

"An important characteristic of fluctuations obeying Poisson statistics is that their standard deviation - the typical fluctuation away from the average in the typical count - equal to the square root of the average count is itself. That is , if 10000 photons are collected on average , the typical fluctuation away from this average number of photons will be about 100 - Typically the counts will range from about 9900 to 10100 If instead on average 100 photons are collected , the variation from count to count will be +/-10."

The variation from count to count of +/-10 is the shot noise itself! Although the numbers of collected photons are positive, the fluctuations (the shot noise) can be positive or negative, depending on the moment, or the cell.


PostPosted: Thu Apr 17, 2014 10:09 pm    Post subject: Reply with quote
In general the Russian counterparts to their German brothers and sisters are inferior, but with the Helios 44 that doesn't seem to be the case!


PostPosted: Fri Apr 18, 2014 2:40 am    Post subject: Reply with quote
Edited

Last edited by bernhardas on Tue May 10, 2016 7:34 am; edited 1 time in total


PostPosted: Fri Apr 18, 2014 10:39 am    Post subject: Reply with quote
Gerald wrote:
sichko wrote:
The signal is essentially a photon count so one normally takes the positive square root. I'm not sure what kind of physical situation would be described by a negative signal to noise ratio - for example.

A useful account of Noise in DSLRs is given by Emile Martinec : http://theory.uchicago.edu/~ejm/pix/20d/tests/noise/index.html

The signal-to-noise ratio, SNR, is defined as:

SNR = (signal power)/(noise power)


I agree. So does Wiki ( http://en.wikipedia.org/wiki/Signal-to-noise_ratio ). Wiki also gives an alternative description ...

SNR = mean/(standard deviation)

which is described as being useful for non-negative variables such as photon counts. Photon Shot Noise follows a Poisson distribution. For a mean or expected photon count, N, the standard deviation, SD, is given by ...

SD = sqrt(N), and also, SNR = N/sqrt(N) = sqrt(N)

I always think of standard deviations as being positive - hence the use of a positive square root. Also, if we use the negative square root the SNR becomes negative. I don't know what that means. And if we use a decibel scale, taking a logarithm of a negative number gives us an imaginary number (?).

My equating Photon Shot Noise with the square root of the Photon count is not arbitrary. This definition of Photon Shot Noise is given explicity by other workers in the field - see for example : http://www.dpreview.com/forums/thread/2544189 . The equations given in this link are the basis of Sensorgen's (See : http://www.sensorgen.info/ ) analysis of DxOMark sensor measurements.


Quote:
SNR is always positive because power is a positive quantity. However, the instantaneous value (or the value at each point, if you're talking about image) of the noise can be negative!


I agree that the value of a single pixel in an image can show a photon count which is less than the mean value for the image as a whole and that the difference is negative. However I wouldn't describe this as negative noise. It (the difference) is one component
of the noise for the whole image. On its own the single pixel value has no noise. It's only negative by reference to the mean of the pixel values across the whole image.


Quote:
This is clear if you read carefully this excerpt from the article you cited,...


There is a transcription error ...

- equal to the square root of the average count is itself. should read - is equal to the square root of the average count itself.

Quote:
... where the author comments on shot noise (highlighted text by me):

"An important characteristic of fluctuations obeying Poisson statistics is that their standard deviation - the typical fluctuation away from the average in the typical count - equal to the square root of the average count is itself. That is , if 10000 photons are collected on average , the typical fluctuation away from this average number of photons will be about 100 - Typically the counts will range from about 9900 to 10100 If instead on average 100 photons are collected , the variation from count to count will be +/-10."


Immediately after your quote Martinec continues .....

Thus, as the signal grows, the photon shot noise also grows, but more slowly; and the signal-to-noise ratio increases as the square root of the number of photons collected. The higher the illumination, the less apparent the shot noise; the lower the illumination, the more apparent it is.

This tells us that, for a photon count N, Photon Shot Noise = sqrt (N), and also, Signal to Noise Ratio, SNR = sqrt(N) - as described earlier.

Quote:
The variation from count to count of +/-10 is the shot noise itself! Although the numbers of collected photons are positive, the fluctuations (the shot noise) can be positive or negative, depending on the moment, or the cell.


So, using Martinec's second example, let's take a count of 90. What does it mean ? How do we calculate the noise ? Is it minus 10 ? It's my understanding that, on its own, it has no noise. It's only by reference to the mean value (100) - calculated by loooking at all the other pixels - that we know that it represents a negative deviation. It's the totality of these deviations, some positive and some negative, which constitutes the noise. Again this is my interpretation. I have no professional qualifications in either Mathematics or Physics. If you could point me to an authoratitive source which takes an alternative view then I would be grateful.

Edit for sp.


PostPosted: Fri Apr 18, 2014 11:01 am    Post subject: Reply with quote
bernhardas wrote:
Gerald wrote:
sichko wrote:
The signal is essentially a photon count so one normally takes the positive square root. I'm not sure what kind of physical situation would be described by a negative signal to noise ratio - for example.

A useful account of Noise in DSLRs is given by Emile Martinec : http://theory.uchicago.edu/~ejm/pix/20d/tests/noise/index.html

The signal-to-noise ratio, SNR, is defined as:

SNR = (signal power)/(noise power)

SNR is always positive because power is a positive quantity. However, the instantaneous value (or the value at each point, if you're talking about image) of the noise can be negative! This is clear if you read carefully this excerpt from the article you cited, where the author comments on shot noise (highlighted text by me):

"An important characteristic of fluctuations obeying Poisson statistics is that their standard deviation - the typical fluctuation away from the average in the typical count - equal to the square root of the average count is itself. That is , if 10000 photons are collected on average , the typical fluctuation away from this average number of photons will be about 100 - Typically the counts will range from about 9900 to 10100 If instead on average 100 photons are collected , the variation from count to count will be +/-10."

The variation from count to count of +/-10 is the shot noise itself! Although the numbers of collected photons are positive, the fluctuations (the shot noise) can be positive or negative, depending on the moment, or the cell.



I think you are both right.



Or maybe both wrong ? Two bald men fighting over a comb ?


PostPosted: Fri Apr 18, 2014 1:10 pm    Post subject: Reply with quote
As a trained analytic chemist, I have go with John's answer Very Happy Very Happy Very Happy Very Happy Very Happy Very Happy Very Happy


PostPosted: Fri Apr 18, 2014 1:44 pm    Post subject: Reply with quote
TijmenDal wrote:
In general the Russian counterparts to their German brothers and sisters are inferior, but with the Helios 44 that doesn't seem to be the case!


The Helios 44 is German by birth. The Helios 44 is a Zeiss Biotar adopted by Russian parents. The merit of the Russians was to reproduce in large numbers a German lens that was state of the art in its time. It's like North Korea began to produce today millions of Zeiss Otus to sell in the international market for $ 100 each. I already pre-ordered mine Very Happy


PostPosted: Fri Apr 18, 2014 2:39 pm    Post subject: Reply with quote
Gerald wrote:
TijmenDal wrote:
In general the Russian counterparts to their German brothers and sisters are inferior, but with the Helios 44 that doesn't seem to be the case!


The Helios 44 is German by birth. The Helios 44 is a Zeiss Biotar adopted by Russian parents. The merit of the Russians was to reproduce in large numbers a German lens that was state of the art in its time. It's like North Korea began to produce today millions of Zeiss Otus to sell in the international market for $ 100 each. I already pre-ordered mine Very Happy


Since I was born in a communist country, I can speak with some authority on the subject. Smile

Categorically speaking, quality control was not a strong suit of a socialist country. I remember when I was young, we had to buy a particular item 2-3 times until we got one that worked well. I imagine lens is no different here, if you had to buy 3-4 to get a good one, it some what neglects the pricing advantage. Mind you, back at the time from where I came from, you can't just walk into a store and "exchange" a defective item. You pretty much was stuck with what you got. I forgot to mention that back then, customer service wasn't one of their strong suit either. Very Happy Very Happy Sad


PostPosted: Fri Apr 18, 2014 2:46 pm    Post subject: Reply with quote
bernhardas wrote:

Manifestation of noise is a variation that can be positive or negative. Because noise starts to be seen in the dark areas easier than in the light ones it feels like it is more of a positive occurrence (can not cause less than 0 in a black bit.

Perfect! Despite the intensity of the shot noise increases with the level of the image, signal-to-noise ratio also increases, so the image looks cleaner in brighter regions.


sichko wrote:
So, using Martinec's second example, let's take a count of 90. What does it mean ? How do we calculate the noise ? Is it minus 10 ?

Yes, at THAT cell, at THAT moment, the noise is -10.


sichko wrote:
It's my understanding that, on its own, it has no noise.

Noise is defined statistically. You cannot tell noise from information by the data of a SINGLE cell in a SINGLE instant.


sichko wrote:
It's only by reference to the mean value (100) - calculated by loooking at all the other pixels - that we know that it represents a negative deviation.

Yes, to characterize a noise you need an ensemble (set of all possible random events) and the associated probability relations.


sichko wrote:
It's the totality of these deviations, some positive and some negative, which constitutes the noise. Again this is my interpretation. I have no professional qualifications in either Mathematics or Physics. If you could point me to an authoratitive source which takes an alternative view then I would be grateful.

Noise is studied within the Theory of Signals in the third or fourth year of an electrical engineering course. It is a difficult subject that requires knowledge in Calculus and Probability Theory, plus a background in physics. I know of no easy way to learn the subject, but perhaps these links may be useful:

http://en.wikipedia.org/wiki/Noise_(electronics)
http://en.wikipedia.org/wiki/Shot_noise
http://en.wikipedia.org/wiki/Image_noise
http://www.cambridgeincolour.com/tutorials/image-noise.htm
http://www.pco.de/fileadmin/user_upload/db/download/pco_cooKe_kb_noise_general_0504.pdf


sichko wrote:
Or maybe both wrong ? Two bald men fighting over a comb ?

Or two camera-less men fighting over a Zeiss Otus? Mr. Green


PostPosted: Thu Apr 24, 2014 1:14 am    Post subject: Reply with quote
Yesterday I compared some Helios 44 lenses, but the original purpose was to see if the newly acquired Helios 44 chrome with 13 blades and F22 is really outstanding among Helios 44 lenses. I had a good impression with this lens (as I posted in my thread several weeks ago http://forum.mflenses.com/helios-44-13-blades-red-p-n0006980-f-22-t65447.html) but I didn't have other Helios 44 to test. I compared it with Helios 44M-4 and 44M-7 and I expected Helios 44 is similar to 44M-7 or even the best, as some people said that this rare Helios 44 is the best.
Tests were done on NEX F3, ISO400.
Shots at f/2


100% crop



I was shocked Shocked . The 44M-4 was obviously the best, in term of sharpness and contrast, it's sharp even at wide open. While 44M-7 was designed to have a better resolution, it's not convincing by seeing these photos, and I was quite disappointed with Helios 44 Sad.

Shots at f/4


100% crop



They are very close in IQ now but still I can see the difference. Helios 44M-4 is so sharp or I was just lucky to get a good copy.

I also wanted to see how these lenses get the flare against direct light



Even though Helios 44 was good to me, the 44M-4 is much better.
Considering the price, 44M-4 is the cheapest and normally you can buy it attached to a Zenit body. I don't know if I should be happy with 44M-4 or upset with the other two Laughing


PostPosted: Thu Apr 24, 2014 10:26 pm    Post subject: Reply with quote
Langstrum wrote:
Yesterday I compared some Helios 44 lenses, but the original purpose was to see if the newly acquired Helios 44 chrome with 13 blades and F22 is really outstanding among Helios 44 lenses.
……………………………
I was shocked . The 44M-4 was obviously the best, in term of sharpness and contrast, it's sharp even at wide open. While 44M-7 was designed to have a better resolution, it's not convincing by seeing these photos, and I was quite disappointed with Helios 44.

Very interesting tests. I believe all Helios 44 luse the same optical construction and therefore should have the same performance, but I am not sure.

I only have one Helios lens, the 44M-4, so I cannot do a more comprehensive test like Langstrum did. Anyway, I took some pictures in Langstrum's style to add some more elements of comparison. Note that the Helios 44 has a larger angular coverage with a FF camera than with an APS-C. The Sony A99 I used has a pixel pitch of 6 microns, while the NEX-F3 has a pixel pitch of 4.8 microns. The difference in pixel pitch is not so great as to invalidate a comparison of the images at a pixel level. I used a focusing distance of about 0.55m, which I believe is similar to that used by Langstrum.

Center



Center F2 - 200% crop



Center F5.6 - 200% crop



Corner



Corner F2 - 200% crop



Corner F5.6 - 200% crop



Analysis of the results
A crop factor of 200% is very revealing. The full image would be about 4 meters wide.

The spherical aberration is well controlled in the Helios 44M-4, so the image is very sharp in the center even wide open. Nonetheless, the image in the center becomes even sharper at F5.6.

In the corners the main aberration is the coma, which produces a characteristic glare when the lens is wide open. The coma glare reduces the image contrast, although the resolution still is quite reasonable. When the lens is stopped down to F5.6, the coma disappears and the image in the corners is almost as good as in the center.


PostPosted: Fri Apr 25, 2014 1:27 am    Post subject: My Helioses Reply with quote
My Helioses

1 Helios 44M-4 KMZ 1976 (declicked)
2 Гелиос 44M-4 KM3 1982
3 Helios 44M-4 Valdai 1983 (the oiled diafragm does not close to f16)
4 MC Helios 44M-4 KMZ 1992 (the multi coating was removed with the exception of the front element and every internal ring was depainted in order to increase reflections)
5 MC Helios 44M-5 Valdai 1992 (a Frankestein one, with glasses coming from MC Helios 44M-5 Valdai (the rear element), MC Helios 44M-7 Valdai (the front element), and MC Helios 44M-6 Valdai (all the rest), in the case of the rear and the front elemets, selected if they have not any scratches)
6 MC Helios 44M-7 Valdai 1994 (the only one with a painted diafragm, or at least it look like it is painted)
7 MC Helios 44M-7 Valdai 1995

For you Gerald to see. They perform very similar. I guess they share they same optical formula, since the elements are interchangeable. But they share little. The coating is very diferent from one copy to the next (even if they are the same nominaly, for example 44M-7). The diferences come in special situations, for example in back ligth setings, where you will see very diferent ghosts and flare. The Pancolar shots were taken at f5.6 and the flash ones were taken at f16.
























PostPosted: Fri Apr 25, 2014 8:02 am    Post subject: Reply with quote
Langstrum wrote:

I was shocked Shocked . The 44M-4 was obviously the best, in term of sharpness and contrast, it's sharp even at wide open. While 44M-7 was designed to have a better resolution, it's not convincing by seeing these photos, and I was quite disappointed with Helios 44 Sad.


Is your Helios 44M-4 multicoated? Is it made by KMZ?
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