Month: March 2022

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Black & White – in Color

Forest in Florida. Good b&w application.

What? Isn’t that contradictory? Isn’t black & white is about the absence of color? I wanted to follow up on a previous article on how we get color information in our digital cameras with a nod to the purity of black and white and emphasize how it is still dependent on color.

Remove the color filter?

I indicated before that our sensors are panchromatic – they respond to the full range of visible light. If we want black & white images, shouldn’t we just take the color filter array off and let each photo site respond to just the grey values?

We could, but most black & white photographers would not be happy with the results. It would be like shooting black & white film. A problem with black and white film is that it eliminates all the information that comes from color. Through interpolation of the Bayer data, we get full data for red, green and blue at each pixel position. If we removed the filter array, we would have only luminosity data. So before even starting, we would be throwing away 2/3 of the data available in our image.

At that point we would have to resort to placing colored filters over the lens, like black & white shooters of old had to do. They did this to “push” the tonal separation in certain directions for the results they wanted. But this filter is global. It affects the whole image rather than being able to do it selectively as we can with digital processing. And it is an irreversible decision we would have to make while we were shooting. Why go backward?

What makes a good b&w image?

Black & white images are a very large and important sub-genre of photography. The styles and results cover a huge range. But I will generalize and say that typically the artists want to achieve a full range of black to white tones in each image with good separation. Think Ansel Adams prints.

Tones refer to the shades of grey in the resulting print. We do a lot of work to selectively control how these tones relate to each other. Typically we want rich black with a little detail preserved in them, bright whites, also containing a little detail, and a full range of distinct tones in between. These mid-range tones give us all the detail and shading.

Tone separation

If one of the goals of black & white photographers is to have high control of the tones, how do we do that? Typically by using the color information. I mentioned putting colored filters over the lens. This was the “way back” solution.

Landscape photographers like Ansel Adams often used a dark red filter to help get the deep toned skies they were known for. Red blocks blue light, forcing all the blue tones toward black.

Digital processing gives us far more control and selectivity than the film photographers had. We don’t have to put the filter over the whole lens and try to envision what the result will be. We can wait and do it on our computer where we have more control, immediate previews, and undo. But all this control would be impossible without having a full color image to work with. As a matter of fact, modern b&w processing starts by working on the color image. Initial tone and range corrections are done in color. Good color makes good b&w.

B&W conversion

Obviously, at some point the color image has to be “mapped” to b&w. This is called b&w conversion. It can be a complicated process. There are many ways to go about the conversion, and each artist has their own favorites. There is no one size fits all.

It is possible to just desaturate the image. This uses a fairly dumb algorithm to just remove the color. It is fast and easy, but it is usually about the worst way to make a good b&w image.

You could use the channels as a source of the conversion. The RGB colors are composed of red, green and blue channels. These can be viewed and manipulated directly in Photoshop. They can often be useful for isolating certain colors to work on. Isolating the red channel would be like putting a strong red filter over the lens.

Lightroom and Photoshop have built in b&w conversion tools. In LIghtroom, choose the Black & White treatment in the Basic panel of the Develop module. This has an interesting optional set of “treatments” to choose from in the grid control right under it. In Photoshop use the B&W adjustment layer.

Both of these have the power of allowing color-selective adjustments. This is huge. Tonal relationships can be controlled to a much greater degree than was possible with film. If we want to just make what were the yellow colors brighter, we can do that. Of course, Photoshop allows using multiple layers with masking to exert even more control.

There are many other techniques, such as channel mixing or gradient maps or plug-ins like Silver Effects to give different and added control. It is actually an embarrassment of riches. This is a great time to be a b&w photographer.

It starts with color

What is common to all of this, though, is that it starts from the color information. Color is key to making most great black & white images.

I sometimes hear a photographer say “that image doesn’t work well in color, convert it to b&w”. Sometimes that works, but I believe it is a bad attitude. B&w is not a means of salvaging mediocre color images. We should select images with a rich spread of tones, great graphic forms, and good color information allowing pleasing tonal separation. Black & white is its own special medium. Remember, though, usually it requires color to work.

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Wonderment

Sense of wonder in a very ordinary scene

Do you still have a sense of wonder? Can you get excited by simple, ordinary things around you? If yours has faded I hope I can refresh your excitement and help you redevelop wonderment.

It came built in

When we were small, most of us had this wonderment. Everything was new and fresh and exciting. An ice cream cone, a kitten, a flower, a ball, a bicycle – they all captivated us. We could go out and play all day with a cardboard box.

But then somewhere along the line, we “grew up”. It is what we were supposed to do. At least, that’s what they said. We became too “mature” for that child-like wonder. Cynicism replaced wonder. Boredom chokes out the joy we had.

Are our lives better off based on cynicism? Perhaps we should try to recapture some of what we had. I believe we can relearn some of this joy and wonder if we work at it.

Change the context

Most of us lead pretty routine, repeatable lives. Making a change to the routine can wake up new ways to see things. Go out for walk. Get up earlier. Sleep in later. Instead of going to one of your normal restaurants fix a picnic and go to a park. Stop and look at a sunset. Really look.

See a road you haven’t been down? Take it. See what’s there. It will probably only take a few minutes, but you expand your viewpoint and feed your curiosity. It’s worth it to me. Even is it is ugly and awful and seems to be a waste, I believe you are better off for breaking the routine and trying it.

Feed your curiosity

Are you still curious? I ask seriously. Many people don’t seem to be curious about the world around them. I think that is part of the cynicism that shuts down the desire to know more. For some it is enough to try to decide what’s for dinner and which TV show to watch.

If you are reading this blog I hope that is not you. I hope you burn with curiosity about a variety of subjects. Let that drive you to do something. Look it up. Build something. Try something new. Read a biography of someone you admire.

Let me give a small example that is completely off topic from art, but relevant to the idea of curiosity. My city is installing fiber to the house broadband throughout the town. So for months there has been strange equipment around putting the conduits underground. I was curious about how that worked so I looked up some articles on horizontal boring. It is pretty fascinating. It is a much better way of installing pipes in areas where there is already a lot of utilities in the way. Now when I see this equipment I have a better idea of how it works and I feel better for taking the time to satisfy my curiosity.

I believe curiosity goes hand in hand with our sense of wonder. They each support the other. As you let your curiosity grow and feel its way in different directions your wonder will grow at what you are discovering. And your wonder encourages you to be more curious.

Slow down

Slowing down can be hard for us. The world pushes us forward at breakneck speed. Faster, be more productive, multi-task, don’t slack off.

But slowing down sometimes (and unplugging from media and social networks) can be very good for us. When we take it slow for a change we see new things. We see things in new ways. Let your mind rest and catch up. Give it some time to relax and think.

And like changing the context, slowing down allows us to see things different. Instead of flashing by with little thought we can take a new look at things around us. Start to really see. Seeing leads to wonder.

One of the things I love to do is show someone a picture and have them say “that’s pretty neat, I’ve never seen that before.” And I point out to them that it is a block from where they are and they’ve passed it 100 times without seeing it. Some people are insulted. But some learn from that that there are interesting things to see all around ir you are receptive.

Travel

This is an easy one. Travel takes us to new locations, out of the norm, maybe out of our comfort zone. This is good. Things seem new and different, and for a while we tend to look around more.

It has always been said that travel is broadening. I agree. The change in perspective and environment and getting out of the usual can be very good for us. One of the hard things is to bring this awakened viewpoint back home. We so quickly fall back into our ruts.

You have control of your attitude. Come back from the trip with a commitment to see your local area as if it was an exotic destination. Sounds silly, but try it.

it’s an attitude

You control your ability to find wonder around you. It is an attitude and something you can practice to improve. Like learning any new habit, it takes time and hard work.

First, you have to decide that a new sense of wonder is worth it. It might take a while to rediscover that spark and recognize it. Then you have to practice finding it. Then you have to keep on pushing yourself to keep looking with fresh eyes, even when everything seem so boring.

Be open to it

Wonderment is really something we find within ourselves. We have to look inside and discover that we are curious and new things we see and find can be exciting and worthwhile.

Climb out of your rut. Take a fresh look around. See with new eyes and a new attitude. Practice, practice, practice.

Somewhere inside is still some of that child-like wonder we used to have. When we bring it out again we have a fresh and exciting life. Be amazed.

Note on the photo: This is a perfectly common and ordinary scene where I live. You would probably walk by it with barely a glance. I have changed it in ways that makes it abstract and difficult to recognize, and to me, it exudes wonder.

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What the Camera Sees

lighting, DOF, shutter speed considerations

One of the important things every photographer has to learn is to see what the camera sees. It is a different process from painting or other visual art. It is a technical process, not only of how the sensor works but the transform of a 3 dimensional world to a 2 dimensional representation. This is part of our art. We have to understand it and be able to predict the results.

Static image

Unless you are shooting video, the end result of the camera’s capture is a static image. That seems like a “duh” to most of us, but it is significant. The entire image is recorded “in one instant”. Yes, I’m ignoring moving shutter slit, HDR, panoramas, time exposures, and other exceptions that can bend the rules.

This “in one instant” is significant because our eyes work in a totally different way. We can only see a small spot at a time. We continually “scan” around a scene to “see” it all. Our brain stitches all these scans together marvelously to give us the impression of a complete scene. We are not aware of it happening.

What difference does it make? Well, there are subtleties. If something moves in real life, our eyes jump to the movement and study it. Movement has a higher priority in our brains than static things.

Our photograph no longer has that movement or flashing lights. It is a flat and static collection of pixels. We have to learn techniques to stimulate the viewer’s eye in other ways. We learn that the eye is drawn to the brightest or highest contrast areas. That informs how to capture the scene and process it to end up with results that help direct our viewer to the parts of the image we want to emphasize. It helps a lot to anticipate what we are going to want to do. This is part of learning how the camera sees.

Depth of field

The static image we create may or may not seem in sharp focus throughout. This is known as depth of field. It is referring to which parts of the scene are in “acceptable” focus. The aperture setting controls the range of this good focus area.

Remember that the 3 main things controlling the exposure of an image are the aperture, the shutter speed, and the ISO setting. The aperture controls the amount of light coming through the lens at any instant. How long the sensor is exposed to the light is the shutter speed. And the ISO setting is the sensitivity of the sensor to the light. A side effect of the aperture setting is the control of effective depth of field.

In real life we do not see limited depth of field. Our eyes focus on one small area at a time. Each spot we focus on is in sharp focus. The resulting image our brain paints is that everything is in focus. Try it. Look around where you are now. Then close your eyes and try to remember which parts were out of focus. Spoiler – there aren’t any. We remember it all in focus.

So this is a big disconnect between what we perceive of a live scene and what we record in a photograph. Some photographers see this as a problem if they cannot keep the entire scene in sharp focus. But intentionally making non-subject areas blurry can also be used for artistic effect. Since this is different from how our eyes see, this creates something that stands out. It can change our perception.

But like it or not, it is something that the camera sees differently and we need to learn how to handle it.

Shutter speed

To our eyes, things seem to be either frozen sharply or “just a blur” moving by. Things are usually only perceived as a blur if we are not paying attention to them.

But for the camera, the shutter is open for a certain amount of time and things are either sharp if they are still or blurred if they are moving. The camera does not understand the scene and it is not smart enough to know what should be sharp.

Let me give an example. Say you are standing beside a road watching a car go by. If we care about the car (wow, a new ______; that would be fun to drive) we are paying attention to it and we perceive it as sharp. To the camera, it is just something moving through the frame while the shutter is open. It has no name or value. The photographer has to determine how to treat this motion. What it should “mean”.

So the photographer may pan with the car to make it appear sharp while the rest of the image is blurred. Or the intent might be for the car to be a blurred streak in the frame. Either way, it is a design decision to be made because the camera records movement differently from us.

The lens

Unlike us, our cameras let us swap out a variety of “eyeballs” – the lens. We have a certain fixed field of view. That is why camera formats have a particular focal length designated as the “normal” lens. For a full frame 35mm camera like I use, the “normal” lens is in the range of 45-50mm, because for this size sensor this corresponds to the field of view we typically see.

But most of our cameras are not limited to that. We can use very wide angle lenses to take in a larger sweep of scene. Or we can use a telephoto lens to bring distant subjects close or to restrict our view to a narrow slice. Or we can use macro lenses to magnify small objects. All these things give us a new perspective on the world that would not be possible with our regular eyes. This is another way the camera sees that we need to learn to use.

Mapping to 2D

The world is 3D. Pictures are 2D. It seems obvious. Yet we must be aware of the transformation that is happening.

In the 3D space we move in, we are acutely aware of depth and movement in many axes – length, width, height, pitch, roll, yaw, and others. We use this information automatically to interpret the world. But it is lost when the scene is captured on our 2D sensor.

We sense depth. “In front of” or “behind” come automatically to us. Our camera is not as smart. The camera sensor records everything in front of it as a flat, static image. The scene is mapped through the particular perspective of the lens being used and onto the flat sensor.

An example to illustrate. This is a classic. You take a picture of your family downtown. The scene looks perfectly fine and normal to you, because you intuitively realize the depth and separation of things. It gives you selective attention. But when you look at the picture there is a very objectionable telephone pole poking out of Uncle Bob’s head. You did not pay attention to that at the time because you “knew” the pole was far behind him and you dismissed it. The camera doesn’t know to ignore it. All pixels are equal.

Light

This is fundamental to our cameras. There has to be a light source. The camera sees only light from a source or light that is reflected or transmitted by objects. But being humans, we interpret the real world as objects. They are “there”. They have mass and form and value and color. Not so to the camera. It doesn’t ascribe meaning to a scene. All a camera can record is light. Our fancy sensor doesn’t see a red ball. It detects, but doesn’t care, that there is a preponderance of light in the red band being recorded.

By its very definition – photo-graphy means writing with light – photography is dependent on light. Our modern sensors are marvelous products. We can shoot at very high ISO and make exposures in almost total darkness. But if any image was recorded, there was some actual light available.

Everything in every image we make is a record of light. More than almost any other art form, photography is dependent on light. Photographers must be intensely sensitive to the direction and quality and color of the light sources that are illuminating our scene. Likewise we must be very aware of the objects the light is falling on, their shape and texture and reflectivity and color.

Learning to see, again

Art in general, and photography in particular, is a lifelong learning. We learn to see creatively. We learn to see compositions and design. And we have to learn to see the way the camera sees. This is the way we capture the image we want.

Note, after writing this I found this good article by David duChemin. He is a great writer.

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It’s A Green World

Colorful mountain stream. It uses a full spectrum of color.

That’s not an environmental statement. As far as our cameras are concerned, green is the “most important” color. I’ll explain why green is foundational to our photography.

Bayer filter

In my previous article I discussed the Bayer Filter and how it allows our digital cameras to reconstruct color. I made a cryptic comment that it was important that there were twice as many green cells as red and blue, but I did not explain. I’ll try to correct that. It is fascinating and highlights some of the brilliance of the Bayer filter design.

“Bryce Bayer’s patent (U.S. Patent No. 3,971,065[6]) in 1976 called the green photosensors luminance-sensitive elements and the red and blue ones chrominance-sensitive elements. He used twice as many green elements as red or blue to mimic the physiology of the human eye. The luminance perception of the human retina uses M and L cone cells combined, during daylight vision, which are most sensitive to green light. ” This is quoted from Wikipedia. Let me try to unpack it a little.

Color description

There are several ways to describe color. Some, like the HSV or HSB or Lab models, separate the concepts of luminance and chrominance. Luminance is the tonal variation of a scene, the brightness range from black to white. Hue and saturation define the color value and purity.

It is all very complicated and, in reality, only interesting to color scientists. I strongly recommend you view this great video that explains how the CIE-1931 diagram was created and what it means. It answered a lot of my questions. As photographers and artists we have to be familiar with some of it. For instance, we have all seen a color wheel like this:

This is a simplified slice through the HSV space at a constant, maximum lightness. Such a model is useful to us because it shows all colors with their most saturated form at the outer edge and least saturated (white, colorless) in the center.

Our eyes

This is nice, but it is all possible colors, not what we really see. As the quote above about Bayer said, the eye is most sensitive to green. Green is right in the middle of the range of light we are sensitive to, the visible spectrum. Here is a plot of our sensitivity to visible color:

Subjective response of typical eye
From: https://lightcolourvision.org/wp-content/uploads/09550-0-A-BL-EN-Sensitivity-of-Human-Eye-to-Visible-Light-80.jpg

It is clear to see, just as Mr. Bayer said, we are most sensitive to green. This is why there are twice as many green cells in the Bayer filter as red and blue. The green is used to measure the luminance, the tone range of the image. This information is critical to deriving the image detail plus the color information through a complex set of transformations.

Why is is so important to get a good measure of luminance? Because of another interesting property of the eye. We are more sensitive to luminance than color. Luminance gives detail. Think of a black and white picture you like. That image is pure luminance information, no color information at all. Yet we see all the fantastic detail and subtle tones perfectly.

Color adds a lot of interest to some images, but we can recognize most subjects perfectly well without it. The opposite is not true in general. If you took all the luminance information out of one of your images it is basically unrecognizable.

Example

Here is a quick example of a typical outdoor scene here in the Colorado mountains. This is the original image:

If I convert it to Lab mode and take just the luminance channel (L) we get a black & white version containing all the detail and tone variation that makes it recognizable:

But now if I copy just the color information (the a and b channels) it is … surreal?:

Why green?

I hope I have demonstrated some of the reasoning behind the Bayer filter. It is a key to our ability to capture color information with our cameras.

The human eye really is most sensitive to green. Having half the color filters in the Bayer filter array as green allows maximum ability to construct the luminance data we are so sensitive to. The magic of the sophisticated built in data processing algorithms let the Raw file converters take all this information and derive the luninance and color information we rely on for our images.

Does this mean we should shoot more green subjects? No. I don’t. Many on my images have little discernible green in them. Take the image at the top of this article. I love the colors in this mountain stream. I don’t look at it and think “green”. The color range is very full, though.

As I write this it is the depth of winter here. Much of the shooting I do right now is very monochrome, almost black and white. The Bayer filter is not there to make our images more green. But if you look at your histogram or channels you may be surprised at how much green data is there. Think about it, a black and white image is 33% green.

Thank you Mr. Bayer and all the scientists and engineers who have done such a great job of perfecting our digital sensing over the decades. You are doing an excellent job!

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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.

Sensor

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.

Feedback?

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 ed@schlotzcreate.com.