Blessing of Technology

A high resolution image with long and difficult exposure

I admit to starting to become a Luddite in some ways. I spent a long career developing and working with advanced technology, but I am starting to object to its misuse, especially by giant corporations and the government who spy and track and infringe our rights. But on the other hand, I occasionally step back and look at where technology has taken the art of photography and have to say “wow”. We live in the best of times for digital imaging. Technology can also be a blessing.

Old books

I think what precipitated this is that I have been going back re-reading my library of photography books. Many of these are by well-known experts of their day. It has been an amazing realization that many of the images in some of them would not be exceptional or even noticed today. And in some, the author’s discussion of the images was mostly about exposure and technical problems. Exposure used to be an overriding concern. We have come a very long way.

In particular, I based this on going back over the following books. This is just a fraction of my library that I have looked at recently.

  • The Fine Print, by Fred Picker, 1975
  • Taking Great Photographs, by John Hedgecoe, 1983
  • The Photograph: Composition and Color Design, by Harald Mante, 2010
  • Learning to See Creatively, by Bryan Peterson, 1988
  • Photography of Natural Things, by Freeman Patterson, 1982
  • The Making of Landscape Photographs, by Charlie Waite, 1992


Most of these books were based on film photography. It amazes me the degree of technical sophistication and planning that was required. For instance, in The Fine Print, most of the discussion about each image was about the film choice, adjusting the camera tilt/shift settings, exposure considerations, development chemistry, and printing tricks.

Do you remember reciprocity failure and how to compensate for exposure degradation on long exposures? Do you know exposure chemistries and how to push process a negative to increase contrast? How about dodging and burning during printing? Or making an unsharp mask?

I skipped this whole generation by shooting slide film during those days. This complex process of color or black & white developing and printing was not for me. And I’m an Engineer. I generally like complexity.

I would say that many of the results I notice in these old books are “thoughtful”. They have to be. It was generally a slow process. It could take an hour to set up for a shot and determine the exposure and anticipate the printing that would have to be done.

I am very thankful I was able to skip this. I am able to be much more spontaneous and intuitive in my shooting. My standards have become very different.

Early digital

Did you know Kodak invented digital photography? I bet they wish they didn’t. It put them out of business.

The first prototype in 1975 was an 8 pound monster the size of a toaster. It took 23 seconds to record a blurry black & white image that had to be read by a separate, larger box.

But unfortunately, for them, Kodak suffered the classic problem of large corporations with entrenched technology: they did not aggressively pursue the new technology for fear of cannibalizing their existing products. They could not convince management that they are going to be cannibalized, and they would be better off doing it themselves. This has put a lot of companies out of business. Who is your buggy whip provider?

Many years of technology improvements and innovation were required before we got an actual digital camera, the Dycam Model 1 in 1990. The first practical digital camera, in my opinion, was the Nikon N8008s in 1992. It had a whopping 1.5 million pixels and could do color!

Collecting pixels is not much benefit unless we can do something with them. Adobe Photoshop 1.0 was released in 1990 on the new Macintosh computer from Apple. Hard to believe there was a time before Photoshop. Or Apple 🙂

Engineering improvements

As a note on something I have observed over a long career: don’t underestimate the power of engineering. The early digital components were just toys, but they gave a hint of what was possible. Most people dismissed them as impractical, predicting they would never be at parity with film. Now even the most die hard film enthusiasts would be hard pressed to make a good argument that film is better.

Engineers and scientists and manufacturers and marketers can do amazing things when there is a market to support them.

An anecdote will illustrate. A friend of mine at HP developed the ink jet printer technology. It was black and white and pretty crude and slow. Not too long after the first one was made, he told me that someday I would take an 8×10 print out of one of these printers, in full color, and it would look every bit as good as a Kodak print. I politely told him he was crazy. But now, here in my studio, I have a 17″x22″ ink jet printer that makes color and black & white prints far better than commercial prints of a few years ago. Much larger printers exist, too. It stretches belief.

State of the art

Look at where we are now (mid 2022 when this was written). I shoot a 47MPix mirrorless camera. The lenses have better optical properties than ever before. They support the full resolving power of the camera sensor.

I can shoot great quality at much higher ISO speeds than has ever been possible.

This camera has abandoned the optical viewfinder and has gone to a marvelous little video display instead. It shows a wealth of information that photographers in the 1990s and before would never have dreamed of. Or I could chose to see the information on the camera back instead.

Since the camera is mirrorless, the sensor is live all the time, continually measuring exposure across the entire frame. No more 18% gray reflected light meter to interpret. And this exposure information is real time displayed for me as a live histogram, focus tracking, etc. Whatever I choose to see.

Exposure is a minor consideration most of the time. I am usually in Aperture Priority mode and the camera’s internal computers do a wonderful job of accurately determining exposure from the data it can see from the whole sensor. Plus I have the histogram to look at to check for abnormal conditions. And the sensor has such an exceptional dynamic range (range of light capture ability from darkest tones, to brightest) that even if I miss the exposure by a stop or 2, I probably have sufficient data to correct it in the computer. Besides, I can immediately review any image to double-check it.

An embarrassment of riches

I am almost embarrassed to have all this power at hand. Compared to image making of a few years ago it is like going from Morse Code to an iPhone.

I don’t worry much about exposure now. I can see what I am about to capture. Even before shooting I know from the histogram that it will be well exposed. I can immediately review any images to verify them. No doubts. No anxiously waiting for the developed film to come back to see if I got the shot.

This technology frees me from most of the mundane technical concerns and lets me concentrate on composition and creativity. The resolution and tonal detail in my images is the best in history. The computer processing power and tools are the best in history. Printing or display of images has never been better. The ability to transfer even huge files anywhere in the world in seconds is amazing and unprecedented.

Thank you, technology! It is a golden age of imaging. We have a blessing of technology.

Out of Gamut

Abstract image with serious gamut problems.

That seems like a strange thing to say. It’s not a phrase you hear in normal conversation. What can it mean? I have written some about how sensors capture color, but I realize I have not mentioned the gnarly problem of color gamut. Unfortunately, I have been bumping into the problem lately, so I had to re-familiarize myself with it. Some of my new work is seriously out of gamut.

What does gamut mean

Most writers avoid this or give overly simplified descriptions. I’m going to treat you as adults, though. If you really are someone who is completely afraid of technology you might want to skip to the end – or ignore the whole subject.

The concept of gamut is really pretty simple, but you need some specialized knowledge and you have to learn some new things about the world.

I have mentioned the CIE-1931 Chromaticity Diagram before. That sounds scary, but you have probably seen the familiar “horseshoe” diagram of colors. I recommend you watch this video to understand how it was derived and what it means. This is the diagram:

CIE-1931 Chromaticity Diagram

After a lot of research and a lot of measurement, scientists determined that this represents all possible colors a typical human can see. Just the hue – color – not the brightness.

Very simply, a gamut is just a representation of what part of this spectrum a particular device can reproduce or capture.

Show me

The next figure shows the horseshoe with some regions overlayed on it.

Add ProPhoto colour space as a "working color space" - Which feature do you need? - DxO Forums

There are 3 triangular regions labeled: sRGB, Adobe RGB, and ProPhoto RGB. They are called color spaces. The diagram is indicating all possible colors that each color space can represent. The smallest one, sRGB, is typical of a computer monitor. It is what will be used when you share a jpg image with someone. It is small but “safe”. We lose a lot of possible colors, but everyone sees roughly the same thing on all their monitors.

Let’s jump to ProPhoto RGB. You can see that it covers the largest part of the horseshoe. In other words, ProPhoto RGB has the largest gamut. It is the best we have for representing image color and most professional photographers use this now. Unless they are doing weddings. That is a different world.

They’re not ideal?

Unfortunately, these color spaces are an ideal. The ProPhoto color space is a model for editing images. No actual devices or printers can give us the entire ProPhoto RBG gamut. Not even close. Most can barely do sRGB.

Here is a diagram of the color space a Canon pro printer can do.

The small horseshoe, labeled 4, is the printer gamut. It is larger then sRGB (3) and, overall, a lot like AdobeRGB (2). Smaller than ProPhoto RGB, which is not listed here.

It looks pretty good, and in general it is. I use one of these printers. But look at what it does not do. Most greens and extremes of cyan and blue and purple and red and orange and yellow cannot be printed. Actually, almost no extremely saturated colors can be printed.

And it is not just printers. Most monitors, even very good ones, are somewhere between sRGB and AdobeRGB spaces. This cannot really be considered a fault of the monitors or printers. The physics and engineering and cost considerations prohibit them from covering the full ideal range.

Any of these colors that I use in an image, that can’t be created by the device I am using, are referred to as “out of gamut”. Outside of the color space the device can produce. This is what I have been running in to lately.

What happens

So what happens when I try to print an image with out of gamut colors? Well, it is not like it blows up or leaves a hole in the page instead of printing anything. Printers and monitors do the best they can. They “remap” the out of gamut colors to the closest they can do. As artists, we have some control over that process, as we will see in the next section.

But the reality is that these out of gamut colors will lose detail, be washed out and without tonal contrast. When we get to looking at the print, we will say “yech, that is terrible”. Then we need to do something about it.

What can we do about it

There are things to do to mitigate the problem. Here is where we need to understand enough about the technology to know what to do.

First, we have tools to help visualize the problem. Both Lightroom Classic and Photoshop have a Soft Proof view. It will simulate the actual output for a particular printer and paper. You can also view gamut clipping for the monitor. Yes, because of gamut problems you may not be seeing the image’s real color information on your monitor.

Both Lightroom and Photoshop have versions of saturation adjustments and hue adjustment. These can help bring the out of control colors back into a printable or viewable range. With practice we can learn to tweak these settings to balance what is possible with what we want to see.

But even if we give up and decide to print images with out of gamut colors, there are options. the print settings have a great feature called “rendering intent”. They are a way to give guidance to the print engine on how we want it to handle these wild colors. Several different rendering intents are available, but the 2 that are most commonly used are Relative and Perceptual.

Rendering Intents

I use Perceptual intent most often, at least in situations where the are significant out of gamut colors. Using the Perceptual directive signifies to the print driver that I am willing to give up complete tonal accuracy for a result that “looks right”. The driver is free to “squish” the color and tone range in proportional amounts to scale the whole image into a printable range. I don’t do product photography or portraits, so I am usually not fanatical about absolute accuracy. How they work this magic is usually kept as a trade secret. But secret or not, it often does a respectable job of producing a good output.

The other common intent is Relative. This basically prints the data without modification, except that it clips out of gamut colors. That sounds severe, but the reality is that most natural scenes will not have any significant gamut problems, so no clipping will occur.

This is a great intent for most types of scenes, because no tonal compression will take place.

The answer

The answer is “your mileage may vary”. Most images of landscapes and people will not have serious out of gamut problems. When you do, this information may help you get the results you want. When you have a problem, turn on the soft proofing and try the Relative and Perceptual rendering intents. Look at the screen to see if one is acceptable. If not, go back and play with saturation and colors .

Why do I have problems? Well, I’m weird. I have been gravitating to extremely vibrant, highly saturated images. I like the look I am trying to get, but it can be hard to get it onto a print. The image at the top of this article is a slice of an image I am working with now. It is seriously out of gamut. I need to work on it a lot more to be able to print it without loss of color detail. Ah, technical limitations.

Is Scaling Bad?

Heavily sharpened image. Many pixels damaged.

I have written about image sharpness before, but I was challenged by a new viewpoint recently. An author I respect made an assertion that gave me pause. He was describing that when you enlarge film it is an optical scaling but digital enlarging requires modifying the information. Implying that modifying information was bad. So I was wondering, is digital scaling bad?

Edges and detail

Let me get two things out of the way. When we are discussing scaling we only mean upscaling, that is, enlarging an image. Shrinking or reducing an image size is not a problem for either film or digital.

The other thing is that the problems from upscaling mostly are edges or fine detailed areas. An edge is a transition from light to dark or dark to light. The more resolution the medium has to keep the abruptness of the transition, the more it looks sharp to us. Areas with gradual tone transitions, like clouds, can be enlarged a lot with little degradation.

Optical scaling

As Mr. Freeman points out, enlarging prints from film relies on optical scaling. An enlarger (big camera, used backward) projects the negative on to print paper on a platen. Lenses and height extensions are used to enlarge the projected image to the desired size.

This is the classic darkroom process that was used for well over 100 years. It still is used by some. It is well proven.

But is is ideal? The optical zooming process enlarges everything. Edges become stretched and blurred, noise is magnified. It is a near exact magnified image of the original piece of film. Unless it is a contact print of an 8×10 inch or larger negative, it has lost resolution. Walk up close to it and it looks blurry and grainy.

Digital scaling

Digital scaling is generally a very different process. Scaling of digital images is usually an intelligent process that does not just multiply the size of everything. It is based on algorithms that look at the spatial frequency of the information – the amount of edges and detail – and scales to preserve that detail.

For instance, one of the common tools for enlarging images is Photoshop. The Image Size dialog is where this is done. When resample is checked, there are 7 choices of scaling algorithms besides the default “Automatic”. I only use Automatic. From what i can figure out it analyzes the image and decides which of the scaling algorithms is optimal. It works very well.

All of these operations modify the original pixels. That is common when working with digital images and it is desirable. As a matter of fact, it is one of the advantages of digital. A non-destructive workflow should be followed to allow re-editing later.

Scaling is normally done as a last step before printing. The file is customized to the final image size, type of print surface, and printer and paper characteristics. So it is typical to do this on a copy of the edited original. In this way the original file is not modified for a particular print size choice.


In digital imaging, it is hard to talk about scaling without talking about sharpening. They go together. The original digital image you load into Lightroom (or whatever you use) looks pretty dull. All of the captured data is there, but it doesn’t look like what we remembered, or want. It is similar to the need for extensive darkroom work to print black & white negatives.

One of the processes in digital photography in general, and after scaling in particular, is sharpening. There are different kinds and degrees of sharpening and several places in the workflow where it is usually applied. It is too complex a subject to talk about here.

But sharpening deals mainly with the contrast around edges. An edge is a sharp increase in contrast. The algorithms increase the contrast where an edge is detected.

This changes the pixels. It’s not like painting out somebody you don’t want in the frame, but it is a change.

By the way, one of the standard sharpening techniques is called Unsharp Mask. It is mind-bending, because it is a way of sharpening an image by blurring it. Non-intuitive. But the point here is this is digital mimicry of a well known technique used by film printers. So the old film masters used the same type of processing tricks to achieve the results they wanted. They even spotted and retouched their negatives.

Modifying pixels

Let me briefly hit on what I think is the basic stumbling block at the bottom of this. Some people have it in their head that there is something wrong or non-artistic about modifying pixels. That is a straw man. It’s as silly as saying you’re not a good oil painter if you mix your colors, since they are no longer the pure colors that came out of the tubes. I have mentioned before that great prints of film images are often very different from the original frame. Does that make them less than genuine?

Art is about achieving the result you want to present to your viewers. How you get there shouldn’t matter much, and any argument of “purity” is strictly a figment of the objector’s imagination.

One of the great benefits of digital imaging is the incredible malleability of the digital data. It can be processed in ways the film masters could only dream of. We as artists need to use this capability to achieve our vision and bring our creativity to the end product.

I am glad I live in an era of digital imaging. I freely modify pixels in any way that seems appropriate to me.

The Making of “Nothing Is Quite What It Seems”

surreal landscape

Today I’m going to discuss the making of this image. I created this abstract image titled “Nothing Is Quite What It Seems” from disparate elements put together to achieve the surreal landscape effect I wanted.

But as the title suggests, nothing is what it seems to be.

Base, Idea

When i saw the thing creating the basic silhouette shapes I knew it needed to be a scene of dead trees in a barren landscape. In reality, though, these shapes are actually cracks in ice on a frozen lake in Colorado.

I framed the scene up to isolate these 2 cracks that looked the most to me like dead trees. The “brush” in the foreground is the near edge of the ice, looking through to some rocks close under the surface.

The processing required some touch-up editing and some dodge and burn and contrast enhancement. There was a little hue-saturation enhancement to bring out more of the yellow rocks.

All of this was done as a smart object in Photoshop. Because I wanted to keep my options open I use smart objects a lot. They give me the freedom to come back and continue editing later. I don’t like to commit permanent changes.


With the basic form set, I started building texture. Tone adjustments in the smart object of the base layer helped. Bringing up the contrast brought forward more of the texture of the ice. This is the dimples and spots all over the image.

To abstract it a little more I used the oil paint filter in Photoshop to soften the edges and give it a more painterly and abstract look.

Color treatment

I knew I wanted to change the color palette and make it look like it could be in an abandoned homestead on the Colorado plains. But I also wanted to layer on more interesting texture. After trying many overlays I settled on a beautiful rusty truck panel. The image I used is part of a 1948 Coleman Truck. Pretty rare, and it was aging beautifully.

The truck had large rust patterns and also areas of old yellow and green paint. Using this to establish the colors across the image worked for me. This truck overlay is also handled as a smart object. Careful blending achieved the look I wanted without it looking like a rusty truck.


The final polishing and tweaking takes a lot of time, even though it doesn’t make sweeping changes. As we used to say in software development, the first 90% of the project takes 100% of the schedule. The last 10% takes the other 100% of the schedule.

There was final dodging and burning to do, bits of masking and retouching. Of course, there was a little bit of final color tweaking to my satisfaction. One of the reasons I use a flexible workflow is that I am prone to tweak things after I have looked at them a while.


A comment on my workflow. Although this is a fairly complex image, nothing is permanently locked down or committed. While writing this I was able to open up all the layers and smart objects and see everything about how they were processed. I could still go in and change or modify anything in the image. And I did make some tweaks. I told you I can’t leave images alone.

And as a very experienced Photoshop user I know new tools will be developed and I will learn new ways of doing things. These will lead to new ways to process images that I will want to take advantage of in the future.

This is the way I choose to work this way on most of my images. It doesn’t take longer and it preserves total flexibility. I need that. I change my mind often!


I like the finished image. It seems to be a surreal Colorado landscape of dead trees, but it contains no trees or plains or anything else that it appears to be. It is truly not quite what it seems. Is this more interesting than a straight shot of the ice?

Lightroom and Photoshop are powerful and addictive tools. Know when to use them and know when to stop. Otherwise you may never stop. It’s a great time to be doing imaging.

How We Get Color Images

Demonstration of crisp, saturated image after demosaicking

Have you ever considered that that great sensor in your camera only sees in black & white? How, then, do we get color images? It turns out that there is some very interesting and complicated Engineering involved behind the scenes. I will try to give an idea of it without getting too technical.


I have discussed digital camera sensors before. They are marvelous, unbelievably complicated and sophisticated chips. But they are, still, a passive collector of photons (light) that falls on them.

An individual imaging site is a small area that collects light and turns it into an electrical signal that can be read and stored. The sensor packs an unimaginable number of these sites into a chip. A “full frame” sensor has an imaging area of 24mm x 36mm, approximately 1 inch by 1.5 inch. My sensor divides that area into 47 million image sites, or pixels. It is called “full frame” because that was the historical size of a 35mm film frame.

But, and this is what most of us miss, the sensor is color blind. It receives and records all frequencies in the visible range. In the film days it would be called panchromatic. That is just a fancy word to say it records in black & white all the tones we typically see across all the colors.

This would be awesome if we only shot black & white. Most of us would reject that.

Need to introduce selective color

So to be able to give us color, the sensor needs to be able to selectively respond to the color ranges we perceive. This is typically Red, Green, and Blue, since these are “primary” colors that can be mixed to create the whole range.

Several techniques have been proposed and tried. A commercially successful implementation is Sigma’s Foveon design. It basically stacks three sensor chips on top of each other. The layers are designed so that shorter wavelengths (blue) are absorbed by the top layer, medium wavelengths (green) are absorbed by the middle layer, and long wavelengths (red) are absorbed by the bottom layer. A very cleaver idea, but it is expensive to manufacture and has problems with noise.

Perfect color separation could be achieved using three sensors with a large color filter over each. Unfortunately this requires a very complex and precise arrangement of mirrors or prisms to split the incoming light to the three sensors. In the process, it reduces the amount of light hitting each sensor, causing problems with image capture range and noise. It is also very difficult and expensive to manufacture and requires 3 full size sensors. Since the sensor is usually the most expensive component of a camera, this prices it out of competition.

Other things have been tried, such as a spinning color wheel over the sensor. If the exposure is captured in sync with the wheel rotation then 3 images could be exposed in rapid sequence giving the 3 colors. Obviously this imposes a lot of limitations on photographers, since the rotation speed has to match the shutter speed. A real problem for very long or very short exposures or moving subjects.

Bayer filter

Thankfully, a practical solution was developed by Bryce Bayer of Kodak. It was patented in 1976, but the patent has expired and the design is freely used by almost all camera manufacturers.

The brilliance of this was to enable color sensing with a single sensor by placing a color filter array (CFA) over the sensor to make each pixel site respond to only one color. You may have seen pictures of it. Here is a representation of the design:

Bayer Filter Array, from Richard Butler, DPReview Mar 29, 2017

The gray grid at the bottom represents the sensor. Each cell is a photo site. Directly over the sensor has been placed an array of colored filters. One filter above each photo site. Each filter is either red or green or blue. Note that there are twice as many green filters as either red or blue. This is important.

But wait, we expect that each pixel in our image contains full RGB color information. With this filter array each pixel only sees one color. How does this work?

It works through some brilliant Engineering with a bit of magic sprinkled in. Full color information for each pixel is constructed by interpolating based on the colors of surrounding pixels.

Restore resolution

Some sophisticated calculations have to be done to calculate the color information for each pixel. This makes each pixel end up with full RGB color values. The process is termed “demosaicking” in tech speak.

I promised to keep it simple. Here is a very simple illustration. In the figure below, if we wanted to derive a value of green for the cell in the center, labeled 5, we could average the green values of the surrounding cells. So an estimate of the green value for cell red5 is (green2+green6+green8+green4)/4

From Demosaicking: Color Filter Array Interpolation, IEEE Signal Processing Magazine, January 2005

This is a very oversimplified description. If you want to get in a little deeper here is an article that talks about some of the considerations without getting too mathematical. Or this one is much deeper but has some good information.

The real world is much more messy. Many special cases have to be accounted for. For instance, sharp edges have to be dealt with specially to avoid color fringing problems. Many other considerations such as balancing the colors complicate the algorithms. It is very sophisticated. The algorithms have been tweaked for over 40 years since Mr. Bayer invented the technique. They are generally very good now.

Thank you, Mr. Bayer. It has proven to be a very useful solution to a difficult problem.

All images interpolated

I want to emphasize a point that basically ALL images are interpolated to reconstruct what we see as the simple RGB data for each pixel. And this interpolation is only one step in the very complicated data transformation pipeline that gets applied to our images “behind the scenes”. This should take away the argument of some of the extreme purists who say they will do nothing in post processing to “damage” the original pixels or to “create” new ones. There really are no original pixels.

I understand your point of view. I used to embrace it, to an extent. But get over it. There is no such thing as “pure” data from your sensor, unless maybe you are using a Foveon-based camera. All images are already interpolated to “create” pixel data before you ever get a chance to even view them in your editor. In addition profiles and lens corrections and other transformations are applied,

Digital imaging is an approximation, an interpretation of the scene the camera was pointed at. The technology has improved to the point that this approximation is quite good. Based on what we have learned, though, we should have a more lenient attitude about post processing the data as much as we feel we need to. It is just data. It is not an image until we say it is, and whatever the data is at that point defines the image.

The image

I chose the image at the head of this article to illustrate that the Bayer filter demosaicking and other image processing steps gives us very good results. The image is detailed and with smooth, well defined color variation and good saturation. And this is a 10 year old sensor and technology. Things are even better now. I am happy with our technology and see no reason to not use it to its fullest.


I felt a need to balance the more philosophical, artsy topics I have been publishing with something more grounded in technology. Especially as I have advocated that the craft is as important as the creativity. I am very curious to know if this is useful to you and interesting. Is my description too simplified? Please let me know. If it is useful, please refer your friends to it. I would love to feel that I am doing useful things for people. If you have trouble with the comment section you can email me at