“Memory of Colors” by Jaime Ocampo-Rangel

A version of this article has also been published on the photography site The Luminous Landscape.

Memory of Colors is the name of large-scale ongoing photography project by the Colombian/French photographer Jaime Ocampo-Rangel. His wife Lia Ocampo-Rangel, a videographer, also contributes to the project.

The project involves recording portraits of individuals from distinctive cultures in remote parts of the world. The photographs show individuals or small groups of people against a monochromatic background. The color of the studio-like backdrop plays a major role in the project because Ocampo-Rangel associates different cultures with different dominant “natural” colors. This can be based on the color of their skin, their clothing, their ornaments or something more abstract.

Girl from Ethiopia's Mursi tribe against a dark brown background (c) Jaime Ocampo-Rangel

The resulting photographs have been displayed as larger-than-life prints at the UNESCO headquarters in Paris and at a Paris art gallery, Polka. A collection of 1300 pictures has just been released in collaboration with Fotopedia in the form of an iPhone/iPad app. There are plans for a traveling exhibition on a Memory of Colors sailing boat that will tour six continents starting in late 2010.

A man with a mission

The indigenous people covered by the project are gradually disappearing: they are losing their cultural identity by merging with other cultures and due to their constant exposure to global cultural influences.

An extreme example is the impact of the discovery of oil in the Arabian peninsula. In the span of less than 50 years, this changed relatively isolated sheikdoms struggling to get by, into an affluent nation with bustling financial centers (Dubai, Abu Dhabi).

A more sweeping historical example is the colonization of much of the world by a series of seafaring European superpowers (Spain, Portugal, France, the United Kingdom, and The Netherlands) between the 16th and 19th centuries. This  lead to the introduction of new cultures, new languages, new religions, new industries and above all new rulers for many continents and subcontinents.

The goal of the Memory of Colors project is to record the disappearing world heritage from the perspective of a former fashion photographer, Jaime Ocampo-Rangel. Jaime also hopes the project will increase the world’s awareness of the value of these cultures among the general public, the indigenous peoples themselves and their respective governments.

This quest is quite similar to that of the Canadian ethnographer Wade Davis (a National Geographic staff member) who stresses that the disappearance of cultural diversity in what he calls the ethnosphere is much more dramatic than the ongoing disappearance of biological diversity in the biosphere. Wade Davis held an impressive 20 minute lecture on endangered cultures at the 2003 TED conference that can be viewed online.

The Photographer

Jaime Ocampo-Rangel was born in Colombia (yes, in South America), and subsequently lived in Miami, Spain, New York and now Paris. In his earlier period he became an accomplished fashion and advertising photographer. As his work from this period is becoming increasingly tricky to find (because he has switched to a very different type of work), I have included one example here.

Example of commercial photography by Jaime Ocampo-Rangel

His transition from Vogue-style fashion photography to almost National Geographic-style photography started in 1999 when he met and photographed the Kogui people of Colombia. The Koguis stayed relatively uninfluenced by the Spanish rulers and by modern society because they withdrew to the mountainous Sierra Nevada de Santa Marta region and avoided contact with “modern” society. Ocampo-Rangel now describes his encounter as “a spiritual and artistic revelation” that lead him to seek out and document such cultures around the world. The project has been ongoing over the past 12 years, initially in parallel to his glamor photography.

It is worth noting that Ocampo-Rangel is not a typical documentary photographer: he has a specific message he wants to convey and he uses his photographic stills to convey an emotional message to the viewer. The still images are thus not intended to tell a story together with a writer’s text and thus differ from the journalistic approach used by say National Geographic or an award-winning Dutch documentary photographer Ilvy Njiokiktjien (who also photographed African tribes like the Mursi depicted above).

Ocampo-Rangel is also not a trained anthropologist or ethnographer like Wade Davis (who also does photography). Although the two think along surprisingly similar lines, Ocampo-Rangel doesn’t worry about scientific niceties like whether the holy Sadhu’s of India or folkloristic French villagers belong to the same category as indigenous tribes such as the Kogui or the Tuaregs of the Sahara. Jaime Ocampo-Rangel is more a portrait photographer with an eye for striking images who wants to convey the message of the disappearing human diversity to a global audience.

To better understand what drives him, you may want to watch one of the video clips made by Jaime and his wife. Jaime’s narrative, with a voice reminiscent of both Sir David Attenborough (the confiding whispering tone) and Jacques Cousteau (the enthusiasm and the accent), give you some impression about his goals and hopes.

Fotopedia’s iOS App

The Fotopedia Memory of Colors app, released on Feb 23, runs on the iPhone and the larger iPad. It costs only 3$ (initially even less). The interactive app covers over 1300 images of 40 cultures throughout the world – obviously significantly more than what can be shown at expositions or even in a book.

The Fotopedia Memory of Colors app on the iPhone

As all Fotopedia products, Memory of Colors allows you to browse the photographs while accessing corresponding Wikipedia articles and Google maps markers. The Google Maps markers are not intended for zooming in and finding exactly where the images were taken: the “pins” just show a general area rather than a specific village or valley. This may be intentional. Many of the tribes are small, and tourism would further impact their way of life.

Fotopedia’s software is easy to use and allows you to browse the images in different ways, the most important here being per culture or per country (see screenshot). To my taste the images could have been more rigorously selected. Sometimes you find very similar images, or even alternative crops of the same photo. This is obviously not a big deal because the browsing is fast and you are free to browse any way you like. But it would have been nicer to distinguish between photos that are worth exhibiting or including in a photo book and those which are useful if you want to actually study the people.

The overall app can be seen as an iPad-based equivalent of  a coffee table book or National Geographic (which is to some degree a coffee table magazine). The information about some tribes is very limited (because of limited Wikipedia content). There is no information from the photographer about individual photographs or photo shoots – unlike what you would expect in a real documentary. To my surprisingly you can save/post/e-mail medium-resolution copies of the images. Photography buffs may be pleased that the EXIF information about lenses and ISO and shutters speeds is still intact.

Photographic Equipment

Although the impact of fancy photographic equipment is overrated by most amateur photographers, it is worth describing the setup that Jaime uses – if only because he lugged this equipment to various remote deserts, the Andes mountains and Siberia. And to highlight the similarity with the equipment used in fashion photography.

The camera is a medium-format Hasselblad camera with, for example, a 100 mm lens (70 mm in full-frame terms). The digital back on the Hasselblad is one of various generations of Phase One digital backs. The backdrop and reflector panels are standard studio stuff. The lighting is normally a single strip-shaped Elinchrome softbox (obviously battery-powered). As in the blue image below, you can see traces of a reflector on the left side of the face. Jaime mentioned that he used to lug 100 kilograms of equipment around, but that he now travels lighter.

The Colors

Colombian (Guambianos) girl (c) Jaime Ocampo-Rangel

The colors are an important part of the project. Although it sounds like something that  emerged at some point during the project, it is surprising that Ocampo-Rangel’s term

Rainbow of human existence

is echoed in (the much less artsy) Wade Davis’ phrase

Polychromatic world of diversity [of people]

I am unqualified to judge whether a color indeed matches the “spirituality” of a particular people, but the strong reliance on background colors does make a difference.

Note that in many cases the background color is consistent – even if the color doesn’t occur in one of the individuals clothing. But sometimes multiple colors are used for a single people. I suppose that a true artist should be allowed break rules, including his own.

It some of the less important images the background cloth is a distractingly wrinkled. As the cloth presumably can’t be ironed during a trip to the desert, it might help to either wrinkle it more (so that it becomes uniform) or to stretch the cloth to minimize the problem.

World Tour and Sponsorship

As you may have concluded by now, Jaime Ocampo-Rangel thinks big. His next major step in the Memory of Colors project is to travel around the world in a sailing boat. The trip is a combination of visiting more indigenous cultures and docking at major cities to display his work. Stills and video will be projected after dark onto the white sails of his boat. The purpose is to spread the word that these valuable cultures are vanishing.

The trip is supported by the UNESCO and other sponsors. The trip is currently planned to start at the Eiffel Tower (situated along the Seine River) due to its proximity to UNESCO’s Paris-based headquarters and to sail to South America (Brazil, Venezuela and his native Colombia), via Panama and the Panama Canal, to visit Australia, Asia, Africa, and to finally cross the Atlantic a second time to hold an exhibition at the United Nations building (situated along the East River in New York City).

In early March 2011 Jaime told me he had already received substantial sponsorship commitments from the UNESCO and other sources. This reassures me that Jaime has the skills to actually get such a dream off the ground. But the project is still searching for additional sponsorship from both individuals (“minimum contribution 5€”) and especially from organizations. This is not just money needed to finance the costs of the voyage, but also to pay for the facilities and time to convert the resulting raw photographic and video material into a book and especially a film for broadcast on various television networks.

The most obvious types of sponsors that come to my mind include (note that I don’t know the list of current sponsors):

  • magazines and museums related to travel and the peoples of the earth,
  • government agencies committed to the welfare of cultural minorities,
  • companies that are strongly associated to color and its emotional impact (paint, fashion, lighting…),
  • the photography industry,
  • the broadcasting, publishing and movie industries (the movie and book side), and
  • travel agencies that specialize in responsible forms of tourism (a tricky one?).

Last but not least, donations can also be done by providing what Jaime called “professional skills”. That is how I got into fixing some of the more glaring bugs in his English (these kind of details don’t have priority for the master). So, for example, support from a professional copywriter or advertising agency would really help get things rolling. The web page on sponsoring indicates how you can contact Jaime. It is OK to contact me about this topic if you have questions, but keep in mind that I do not represent him. I am merely occasionally in touch with Jaime to help out a bit.

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Competition results

The results of my (and our) photos in the yearly exhibition of Fotogroup Waalre can be found here. The text is in Dutch, but there are enough photos to look at.

My “Power Lines” photo reached the top 10% in a national photo competition. Info (also in Dutch) here. Three submissions from our photo club reached the top 10% and the photographers received a fancy certificate. These results are better than the year before.

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Exposition of Peter’s Photography

A poster for the exposition

Thirty of my photos are on display as large prints at a local art gallery (in Nuenen, near Eindhoven). Paintings by Jaap Jonker are also exhibited during the same period. The gallery is open for the general public on four Sunday afternoons (see poster) between 13:00 and 17:00. Let me know if you are planning to attend (I will try to be present to show you around).

The Photos

The exhibition covers four themes: Nature, People, Architecture and Abstract. Each theme is displayed in a separate room. The commonality in style across all photos is that the images strive for simplicity and elegance. Furthermore all of the photos are pretty close to what the photographer actually saw: there are no posed photos, no staged setups or no major digital manipulations.

You can see some of the photos as thumbnails at the top of this page (or here if you are reading this via the site’s home page).

Prints

Just like digital cameras have largely replaced film, digital printing techniques have pushed out dark room printing techniques in “fine art” photography during the last five years: the results are more accurate colors, wider color gamut, sharper images, better repeatability and more color stability.

My prints are printed using an Epson Stylus Pro 3880 printer using 8 color pigment ink printing. The series is printed on Ilford Galerie paper (310 g/m2, “Gold Fibre Silk” – but the name is meaningless). The mattes (passe-partouts) are acid-free and the matteing technique are based on archiving techniques (a variation of Reichmann/Schewe‘s approach). In practice this means that experts claim that such prints should look fine for 100-200 years. This may all be overdone, but I don’t expect to have exhibitions of this size too often.

The prints are framed in sizes of 50×50 cm, 50×60 cm and 50×67 cm (and one 50×70 cm).

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Review of Fine Art Printing video tutorial

After two technical postings, here is some lighter reading. Over the holidays, I bought the downloadable tutorial video from the Luminous Landscape site called

From Camera To Print – Fine Art Printing Tutorial

The tutorial is presented by two accomplished photographers, Michael Reichmann and Jeff Schewe, who talk you through the steps involved in making gallery-quality “fine art” prints. This means prints for sale, prints for collectors or prints for exhibitions. Or maybe just great prints to hang on your own wall.

Printing at this level is about controlling numerous details, but also about fussing about subtle nuances and achieving repeatability. Unfortunately, even the larger differences (like the difference between prints on glossy and matte paper) are as hard to show directly on a video. But the video manages to explain all this anyway, partly by showing enlarged or exaggerated versions (e.g. for sharpening workflow) on a computer monitor.

Overall Impressions

I am a bit of a fan of the tutorial style of Reichmann, Schewe and Christopher Sanderson (the invisible videographer). I tend to be pretty selective about what I read and watch: especially when the total series of 24 files takes a little under 7 hours to watch in its entirity. But this video tutorial is un-American in the good sense of the word – it doesn’t remind you of US talk show hosts.

One of their strengths, apart from their occasional comic Statler/Waldorf moments, are that together the two presenters cover the artsy side of fine-art photography (mainly Michael Reichmann) all the way to the technical side (mainly Jeff Schewe).

Until July 2010, Michael Reichmann operated a gallery in Toronto (yes, Canada) where his work was on display. He now still sells his prints via the Internet (I own a small one). Reichmann is best known for his reviews on the Luminous Landscape website, for his landscape photography, and apparently also for training, workshops and the like.

Jeff Schewe‘s background includes a career in advertising photography. He also consults for Epson’s professional printing division (and in return gets all the printers and supplies that “he can eat”). To avoid an Epson bias, the competing printers from Canon and Hewlett-Packard are presented by Michael. Jeff also has close ties to the Adobe development teams for Photoshop and Lightroom and has co-developed a notable sharpening plug-in and has written several Photoshop-related books.

What works really nicely, is that these video sessions have a general plan but are still pretty much authentic dialogs: Michael stresses why he and gallery visitors like the feel of matte paper, Jeff reminds him that that is less relevant once the print is behind glass, etc. So it is just like hearing two experts improvising to summarize their views and experiencee in a clear and accurate manner.

Scope

You might expect a tutorial about printing to be about printers. In reality, printers themselves do not feature prominently in the tutorial. In fact, believe it or not, you never see someone loading paper into a printer or a printer actually printing. Instead the tutorial covers everything that it takes to make high quality art prints:

  • printer, ink, paper – briefly
  • color management & profiling (camera, screen, scanner, printer) – extensively
  • sharpening techniques – reasonably extensively
  • print settings (mainly Photoshop and Lightroom, both Mac & PC) – extensively
  • soft proofing in Photoshop – extensively
  • matteing and framing – extensively
  • workflow – extensively

Interestingly, in the final wrap-up, Jeff acknowledges that getting all the details right (in terms of calibration and particularly software settings) is still a “gross inconvenience” and expresses the hope that things will get simpler in the near future.

Comments and suggestions

  • The video was made in 2007. At that time, for example, the Epson 3800 and the HP B9180 were both new. The former has been replaced by the 3880 and HP discontinued it’s 17″ product line. Back then Lightroom was at version 1.1. In general,  the fact that the video shows its age on such details is not too disturbing. But maybe a text file with some notes might help until there is a newer version of the tutorial. Examples:
    • LR didn’t have soft proofing in 2007. The authors expected this glaring omission to be resolved soon. That still hasn’t happened (thank you, Adobe).
    • LR 1.1 had little control for output sharpening. Tot what degree are the new options in LR 2.3 and 3.3 good enough? Jeff has done some consultancy towards Lightroom to get their sharpening techniques more leading edge. But the viewer cannot tell to what degree that has helped to simplify the overall process in the subsequent years: should you still do output sharpening in Photoshop (possibly using Jeff’s plug-in) as explained in the video?
    • The video demonstrates what Jeff calls the “old way” where you generate a new version of the image that is optimized for a particular print setup. Obviously Lightroom (with it’s parametric philosophy) was already available, but what workflow to use for output sharpening now?
    • ICC profiles from 3rd party paper manufacturer’s were described by Jeff as being generally less reliable. Is that assessment still justified? In fact, was the assessment fair at the time? For me it is not obvious that say Epson is better at creating profiles than paper specialists like Ilford or Hahnemuhle.
  • The tutorial shows how to cut mattes (= “passe-partouts” in other major global languages like Dutch, Finnish and even French) using a wall-mounted $2000+ Speed Matte system. This is fine for high volume, but it would be nice to at least get some tips on how/whether to use a low-cost matte cutter based on a ruler and sliding matteing knife.
  • At the very end, the tutorial points you to one of the Luminious Landscape Video Journals for a list of brands of the stuff used in the tutorial. These are largely brands of minor things like adhesive tape or foam board. That list is probably only useful if you live in the US or Canada, but the video should be self-contained without this loose end, even though it is optionally sold in combination with that extra video (which I didn’t order).
  • I wouldn’t mind a discussion of how to load paper into a printer. In particular, printers have 2 or 3 ways to load sheet paper. Some have roll feeds. Many have fuzzy restrictions about what feed to use for what type of paper. Or the ability to adjust (argggh) “platten gaps” in the printer driver. Nothing is quite as simple as it seems in “fine art” printing, especially when every sheet wasted comes at quite a cost. And high-end printers don’t come with much documentation. Incidentally, why not bundle the Luminous Landscape video in DVD form with a $1000+ fine-art printer…

Notes on the main things that I learned

The video essentially covered what I was looking for in a useful and enjoyable fashion. Here are the notes I made on key points that I personally learned and need to remember (note that I was pretty up to speed on color management and Lightroom already):

  1. The black density (DMax) of matte paper is lower than of glossy paper: maybe 1.7 versus 2.4. I have seen this myself, but a confirmation that this is normal is nice.
  2. Borderless printing is strongly discouraged (because of framing implications).
  3. Really glossy papers are strongly discouraged (despite giving a better contrast). Not sure I agree. In the darkroom days, glossy was quite mainstream.
  4. Lightroom contains calibration profiles for cameras. This is automated/hidden from the user. You can’t change it (actually a workaround is demonstrated if you need to calibrate your specific camera instance).
  5. Even if your photo is sharp, you need to add a degree of output sharpening. It isn’t explained why this level of manual intervention is needed in the first place. But it is important to know that this is normal and needed.
  6. You should print at resolutions between 180 and 480 Pixels/Inch. Lower requires special upscaling tricks (and consider adding noise) which can get horribly complicated. Higher resolutions don’t help and only make life difficult for the printer driver.
  7. There is a nice demo on how to do high-end matteing. Including signing the matte in pencil, and signing the print in ink (even though that will presumably never be seen again). And particularly mounting the actual print in such a way that it can be removed if ever needed. Fancy.
  8. Use “simulate paper” and “simulate black level” when soft proofing. I still have my doubts about the “simulate paper” one though (as it predicts much more yellow results than the actual print).

Conclusion

If you own a serious (pigment-based, 17″ or better) printer and create or want to create gallery-quality prints, I can recommend this video tutorial: “optimal” and repeatable printing is simply a tricky business. Even if you find only a few tips for things you then do differently in the future, the time is well-spent. And it is almost as fun to watch as a live course (although you can’t see detailed print samples here). The video is obviously much cheaper than a one-day workshop, but you learn just as much. Unfortunately you can’t talk to the presenters, but you can replay parts is needed.

http://en.wikipedia.org/wiki/Statler_and_Waldorf
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Notes on a tutorial about image sensor noise

During late 2009 and all of 2010, Albert Theuwissen published an 26-part series of postings on image sensor noise on his Harvest Imaging website. The series explores various sources of image sensor noise and their relationship to signal strength. The series targets sensor designers, and those who use sensors in challenging applications. Probably many of the Harvest Imaging readers know Prof. Theuwissen from his courses, workshops and conferences.

The series centers around a proprietary simulation model (written in Matlab/C?) where Theuwissen selectively isolates each noise source encountered in a sensor to show its impact on overall image noise. Every installment of the series centers around a graph (James Janesick’s Photon Transfer Curve, PTC) that plots sensor noise against sensor signal. The graph is thus closely related to the signal-to-noise ratio, but the graph stresses how the ratio varies

  • as the sensor is exposed to darkness for varying durations, and
  • as the sensor is exposed to light for varying exposures.

If you are not a sensor expert, you can try to use the series to learn about sensor behavior – provided you can handle a bunch of basic formulas and are willing to learn the associated terminology (which is is not entirely consistent across postings). Hence these notes which try to follow the terminology used in later postings (where up to 15 noise sources needed to be distinguished).

Incidentally, the word “Harvest” in the domain name of Albert’s website is after the title of a Neil Young album: Prof. Theuwissen is somewhat of a Neil Young fan.

Abbreviations

  • CDS = correlated double sampling (a kind of self-calibration technique with differential amplifiers, see Wikipedia)
  • DN = digital number (simply the digital value read out after measuring the analog signal)
  • DSNU = Dark Signal Non-Uniformity (differences in dark current signal build-up due to variations between individual pixels)
  • FPN = fixed pattern noise (small pixel-to-pixel deviations that don’t change over time)
  • k = gain in DN/electron
  • PRNU = photo-response non-uniformity (differences is pixel sensitivity to light)
  • PTC = photon transfer curve = signal versus noise graph
  • RTS = random telegraph signals (random jumping between fixed output levels)

Overview of sources of dark noise (see also posting p=358)

The following table lists all the noise sources that occur in absolute darkness. They also occur when there is light (but with light there are extra noise sources). They are discussed in more detail below.

All noise sources are measured by resetting the pixels, and then reading out the pixel after a short or longer delay. I classified the noise sources based on their time behavior (the table columns) and their source (the table rows). The central message of the Harvest Imaging series is that you can distinguish these noise sources in actual measurements by analyzing noise build-up over time (to distinguish the table columns) and by different ways of averaging the individual pixel measurements (to isolate fixed sensor line and column pattern noise).

The conclusion of the series is that you can distinguish many of the noise sources by appropriate measurements on a sensor. And that the estimated parameter values can be pretty accurate.

Fixed-Pattern Noise Non-uniformity
Noise
Temporal Noise Shot Noise (Poisson statistics)
Scales with Constant value time or dark current
Constant std dev Sqrt(time) or Sqrt(dark signal)
Pixel-level Pixel defects (p=329) & RTS (p=344) & Pixel FPN (p=358) Dark Signal Non Uniformity (p=84) Temporal
pixel noise
(p=211)
Dark current shot noise (p=48)
Output-level Amplifier offset (p=154) Output amplifier (p=263)
Row-level Row FPN (p=243) Temporal
row noise
(p=243)
Column-level Column FPN (p=229) Temporal
column noise
(p=229)

The Amplifier offset (p=154) has such a bad effect on low signal measurements that it is assumed to be corrected away in most of the PTC graphs.

A noise source not listed above, Saturation Non-Uniformity (p=142), is only relevant for severely overexposed pixels. This can happen during normal exposures, but this part of the dynamic range is normally hidden from the user because of its non-linearity and non-uniformity.

Careful: there may be multiple definitions of Temporal Pixel Noise: including or excluding temporal row/column noises. When you just measure the pixel noise, you get “including”, but when you do a lot of analysis or create a synthetic model, you get “excluding”. A similar problem may exist for pixel-level FPN.

The sample sensor used in all computations

Calculations are done on a hypothetical 160×120 pixel sensor. Given the assumed full-well capacity of 17,500 electrons, the pixels may have a pitch of around 3-5 μm. So the data would correspond to a small section of a larger sensor with pixel dimensions that are likely between compact camera pixels and SLR camera pixels. The physical dimensions are not directly relevant for any of the calculations.

1. Dark Current Shot Noise (p=48)

  • Dark Current is what a sensor sees during long exposures with no light at all.
  • Signal proportional to exposure time (leakage?)
  • Noise = sqrt(dark signal) ; Poisson
  • measurement of PTC can tell you k of the system (here 0.15 DN/e-)

2. Dark Signal Non-Uniformity (p=84)

  • Noise to pixel having varying levels of dark current (non-uniformity)
  • Noise = fraction of Dark Signal (here 15%)
  • careful: the terminology switches back and forth between FPN and DSNU
  • later in the series this is simply known as DSNU

3. PTN Curve and Temperature (p=120)

  • Dark Signal and its noise both scale exponentially with temperate
  • The PTN graph shows their ratio, and is thus temperature independent

4. Anti-blooming (p=142)

  • Anti-blooming is a kind of safety value for pixels that overexposed.
  • It causes non-linear response above what the sensor considers to be white.
  • The threshold for anti-blooming introduces significant FPN
  • In the anti-blooming range, noise drops because temporal noise is clamped.

5. Amplifier Offset (p=154)

  • Offsets “in analog circuitry” can mess up readings, especially of low DN values.
  • They simply need to be (accurately) compensated

6. Pixel Noise (p=211)

  • At zero integration time, there should be 0 dark current signal and 0 DSNU noise.
  • In reality, there is signal due to imperfect offset compensation.
  • And noise because there is temporal noise in the analog chain.

7. Column FPN and Temporal Noise (p=229)

  • Columns (by def.) share the same bias and readout circuitry. Can thus have FPN.
  • Column FPN will exceed dark current noise for short enough (e.g. 1 second) integration times
  • Can isolate Column noise by averaging all pixel FPN across all pixels in a column

8. Row Noise (p=243)

  • Defined as row-to-row variation in the average of all pixels in a row.
  • Row FPN noise may repeat very N (e.g. 16) columns. No explanation. Timing?
  • Can be measured by calculating Fourier transform of noise-versus-row#.

9. Amplifier Noise (p=263)

  • Gives non-zero (e.g. 1.2 DN) noise at zero exposure time.
  • Differential amplifier has “correlated double sampling” trick.
  • CDS should reduce offset and low-frequency noise sources.

10. Defective Pixels (p=329)

  • Defective pixels are “stuck at” 0 or 1 and give extreme spikes in the image
  • Are normally compensated for by digital processing (e.g. replaced by estimate)
  • After compensation, their impact on statistics should be small

11. RTS Noise (p=344)

  • Random Telegraph Signals pixels are pixels that hop slowly between fixed levels.
  • Their cause is not really understood.
  • e.g. 5x more RTS pixels than stuck-at pixels
  • RTS noise hardly visible in the PTC curve. Why?

12. Integral Fixed-Pattern Noise (p=358)

  • isolate FPN by averaging enough frames, thus getting rid of temporal noise
  • uncorrected, defective pixels dominate the (short integration time) FPN
  • can measure Row and Column contributions by averaging over pixels & frames
  • can isolate Pixel FPN noise by subtracting (squared) Row & Column FPN noise
  • noise parameters can be estimated from the simulated images (about 10% off)

13. Integral Temporal Noise (p=386)

  • standard deviation is calculated per pixel (by using 100 images)
  • this removes all FPN effects
  • at long integration times, dark current shot noise dominates
  • at short integration times, noise from pixel/row/column electronics dominates
  • not directly possible to split temporal noise in pixel/row/column components

14. Dark image data files are available (p=417)

  • due to public demand, Theuwissen provided a 162 MByte file with all images generated by the simulations (at different integration times in the dark)
  • the download does not include the simulation model that generated the images

15. Let there be light (p=430)

  • initial simulations are without any noise sources on the silicon
  • signal is proportional to integration time (until close to saturation)
  • photons result on average in e.g. 0.3 free electron (quantum efficiency)
  • noise is shot noise and proportional to sqrt(time) or sqrt(photons)

Additional noise sources due to light

Fixed-Pattern Noise Non-uniformity
Noise
Temporal Noise Shot Noise (Poisson statistics)
Scales with Constant value time or photons Constant std dev Sqrt(time) or Sqrt(photons)
Pixel-level Pixel-level FPN (p=501), Pixel Defects (p=527), RTS Defects (p=546) Photo-response non-uniformity (p=454) Photon shot noise (p=430)
Output-level Output-level FPN (p=501)
Row-level Row-level FPN (p=501)
Column-level Column-level FPN (p=501)

Note that the Fixed-Pattern Noise column are not new noise sources: they exist (presumably with the same magnitude) in the absence of light but are measured again below in the presence of light. PRNU and Photon Shot Noise, in contrast, are really new noise sources that increase noise when photons fall on the sensor.

In addition, photons obviously also cause a signal component (which looks like dark current, but is proportional to the photon flux) which is what the sensor is meant to measure in the first place. It is not shown in the above table because it is not a noise source.

16. Photon shot noise (p=430)

  • exposure times here 0.6 s (compared to 60 s for dark current measurements)
  • linear signal response (to time=photos=electrons) until pixel saturation is reached
  • noise is shot noise (Poisson = sqrt(signal)) when all sensor noise is eliminated
  • PTC graph can be used to estimate system-level gain k

17. Photo-response non-uniformity (PRNU p=454)

  • cause is non-uniformities such as varying pixel size or varying quantum efficiency
  • noise is proportional to signal (e.g. 3%)
  • when the signal saturates, PRNU continues to increase (artifact of model?)
  • I checked with Theuwissen: due to saturation FPN

18. Combining both sources of non-uniformity (PRNU & DSNU p=472)

  • Dark signal and photo response non-uniformity look pretty much the same
  • At high light intensity (short integration times) PRNU dominates e.g. by 100x
  • And photon shot noise dominates the dark current shot noise

19. Combining both sources of non-uniformity (PRNU & DSNU p=486)

  • At low light (long integration times) DSNU becomes comparable to PRNU
  • This causes fixed-pattern noise to increase
  • To distinguish the two you need to vary light intensity
  • Turning on both shot noise sources increases the noise level and signal level
  • So the PTC curve doesn’t shift: if you know the signal, you known the shot noise – regardless of whether the signal is dark current, photons or a mix.

20. Pixel/Row/Column FPN noise (p=501)

  • FPN Noise is a sum of Pixel/Row/Column FPN noise
  • Row and Column data can be isolated by suitable averaging
  • Pixel noise can be calculated by subtracting squared row and column values
  • Looks like the Row-level noise value has a typo (not a big deal, I reported this)
  • This posting doesn’t distinguish between dark current FPN and photo-related FPN, but this could be done by varying the light intensity.

21. Temporal Pixel/Output/Row/Column noise (PORC, p=516)

  • adding Pixel/Output/Row/Column temporal noise dominates very short exposures, but is negligible at longer exposures
  • it is probably not feasible to isolate Pixel/Row/Column noise sources
  • Output noise might be measurable by measuring noise far into saturation

22. Stuck-at Pixel Defects (p=527)

  • Story very similar to the defective pixels in the dark
  • uncorrected, defective pixels dominate very brief exposures
  • they can be corrected efficiently using a processor
  • once corrected, they hardly have an impact on fixed pattern noise

23. RTS Defects (p=546)

  • RTS defects had little impact in the dark, so the same applies with light
  • Still unclear why an RTS pixel contributes at least one order of mag less noise than a defective pixel. I posted the question.

24. FPN noise estimation using the complete model (p=559)

  • 15 noise sources are included. The underlined ones are relevant when measuring fixed-pattern noise:
  1. overall amplifier offset *
  2. dark current shot noise
  3. Dark Signal Non-Uniformity
  4. saturation non-uniformity *
  5. Pixel temporal noise
  6. Output amplifier temporal noise
  7. Row temporal noise
  8. Column temporal noise
  9. FPN for pixels *
  10. FPN for rows *
  11. FPN for columns *
  12. PRNU *
  13. photon shot noise
  14. defective pixels*
  15. RTS pixels
  • until corrected, offset dominates low exposure FPN
  • the splitting of Pixel/Row/Column noise is done just like p=501
  • and results in very similar numbers (no typo this time)
  • the sources that could be estimated are marked with a *
    • DSNU would look just like PRNU in the PTC, but should be negligible
    • RTS should be negligible

25. Temporal noise estimation using the complete model (p=578)

  • 15 noise sources are included. The underlined ones are relevant when measuring temporal noise:
  1. overall amplifier offset
  2. dark current shot noise Medium
  3. Dark Signal Non-Uniformity
  4. saturation non-uniformity
  5. Pixel temporal noise Low
  6. Output amplifier temporal noise Low
  7. Row temporal noise Low
  8. Column temporal noise Low
  9. FPN for pixels
  10. FPN for rows
  11. FPN for columns
  12. PRNU
  13. photon shot noise Medium
  14. defective pixels
  15. RTS pixels
  • At low exposure to light, the sources marked Low dominate. They cannot be distinguished using the PTC. Added together they are called temporal pixel noise, but this is ambiguous.
  • At higher exposure to light, photon shot noise dominates. Dark current shot noise should be negligible for these short integration times.
  • At high exposure the pixels go into saturation and there is little noise left (you might be able to estimate the output amplifier noise if you really cared)

26. Lit image data files are available (p=588)

  • Due to public demand, Theuwissen provided a 141 MByte file with all images generated by the simulations (at different exposure times)
  • The download does not include the simulation model that generated the images
  • This is the counterpart to the dark data files provided in p=417
  • The images should look pretty much the same as the dark images (because the ones in the dark are at much longer integration times). I haven’t checked.
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