Category Archives: Lightroom

The 5 Most Used Photo Enhancement Techniques

We have been watching ourselves over our shoulders – yeah, kinda weird, right? Our goal is to figure out what it is we do the most to fix and beautify our photography – night photography in particular.  We cover nearly all of these topics in greater detail in each of our NP150 – Photo Manipulation Webinars but we will hit the highlights here. So what are the 5 most used “tricks”?

  1. White Balance / Color correction
  2. Noise Reduction
  3. Exposure and Contrast enhancement
  4. Sharpening (and de-sharpening)
  5. Healing and Cloning

The first two topics are tackled below. The next two in the following article.  Healing and cloning will get it’s own short article.

First to be clear our goal is usually to make a compelling photo, not merely to represent reality. We do prefer realistic over bizarre, but we are not opposed to removing telephone wires and other distractions.  We DO prefer natural looking scenes and eschew the over-the-top contrast and color saturation that seems to be the rage these days.

White Balance

We hate to be the first to break the news, but your camera is pretty clueless about what color white is. The camera will take its best guess. Our experience with night photography is that the camera choice is usually wrong – or at least unappealing. At its core white balance requires adjusting the red, green, and blue colors so that an area that should have a neutral gray or white color is actually gray – not tinged red, blue or green. When shooting at night, understand that every light in the scene – including stars – has a different color bias (tint). Sodium vapor lights for example are horrendous. Sodium vapor lights used in many streetlights are predominately yellow-brown and almost monochromatic. Under sodium vapor lights it will be impossible to achieve a natural color spectrum. You may want to adjust different parts of a night image separately.  You may have to compromise and have a scene that has a bias of a pleasing kind rather than the ugly variety.

Correcting White Balance

The easiest way to make the adjustment in Lightroom or Adobe Camera Raw is with the White Balance tool.  It looks like an eye dropper. You click the tool, then click a gray area of the photo – if there is something that should be gray in the photo, that is. Even a white area is fine (but not an overexposed white). Stars are generally not good choices for gray-scale selection both because they are often over exposed and because many of them are NOT white! In the example below you see a photo of the Ocracoke Lighthouse which is definitely white, not the orange that resulted from sodium vapor lights.

LR_WhiteBalanceExample

The White Balance tool in Adobe Lightroom

ACR_WBtool

Adobe Camera Raw White Balance Tool

The dubious result of getting the lighthouse to its proper color via the white balance tool appears below. The sky and stars are unnaturally and artificially blue.  There are several solutions to this problem: color correct the lighthouse separately or compromise by warming (increasing the temperature) of the selected balance. Or try again by clicking elsewhere!

LR_WhiteBalanceExample_After

By selecting a different location for the gray sample tool a better compromise can be achieved as shown in the photo below. An examination of the scene reveals that the lighthouse is directly lit while the shed (near the bottom of the shot) is not. The shed and other areas in shadow are lit by ambient light reflected from many sources – including sky glow. After selecting the shadow area the remaining white imbalance of the lighthouse can be handled by desaturating using the local adjustment brush – or leave it like it is.

LR_WB_ChoiceB

 

White Balance Correction in Photoshop

The same eyedropper style adjustment can be found in Photoshop, but you’ll have to hunt for it because the “Color Balance” Adjustment is not where you’ll find it!

PhotoshopWB_a

White Balance (gray point) selector of Curves in Photoshop

White Balance (gray point) selector of Curves in Photoshop. The gray point selector is also in the Levels adjustment.

Note that in Photoshop you have two ways to go: use an adjustment layer, or use an Image -> Adjustments -> Curves (or Levels). We recommend using an Adjustment Layer because you can paint on the mask to control the effect and that makes it easy to adjust different parts of the image separately.  To adjust areas separately in Lightroom, use the Local Correction brush and adjust the white balance slider.

Selective Color Balance Correction

Consider the following photo. With the new flight rules, you can use your camera while the plane is taking off or landing.  Here the plane is landing at San Jose International Airport. There are two things about it that are good candidates for fixing. The first is the distracting glare of reflection from light inside the airplane (that’s due to Virgin America’s “Purple Ambiance”).  We’d like to get rid of the distraction and it’s clear we will not be able to simply crop it out without giving up some of the interesting details.

Landing

Glare from internal reflections leaves a blue cast. There is also noise in this one second, ISO 1600 exposure.

The second thing that is noticeable in the 100% (Zoomed view) is the colorful noise in the dark (and light) areas of the photo.  No sky has grit in it – at least not like that!

gs_2014-01-28_085553

100% View of the noise near the wing.

We can tackle both problems separately or at once.  Selectively desaturating, and slightly darkening the blue glare is simple in Lightroom.  Select the adjustment brush (it looks like a face powder brush right below the “Histogram”), dial down the saturation, and slightly dial back the exposure. Then paint on the image where we want the change to occur.  It may be useful to adjust the brush size, density and feathering. Here some feathering is important. We will not try to also increase the noise processing here, because the whole image needs some despeckling.

Below the mask shows where we painted – and not particularly carefully, either!  The Saturation was turned down to -69, and the exposure by almost a full stop. In a brighter sky we might not have been able to darken the touched up area as aggressively.

Attacking the Glare with Local Adjustments in Lightroom

Attacking the Glare with Local Adjustments in Lightroom

The next thing we want to address is the noise. It’s everywhere in this photo. As we will learn in the next article, we often use noise reduction for smoothing things like blue (or dark) skies and in shadows where you would not expect to find details.  Using Adobe Camera Raw for saturation, exposure and noise reduction works the same way as in Lightroom it’s just that the adjustment brush is shaped differently and found in a different place.

The adjustment brush in Adobe Camera Raw

The adjustment brush in Adobe Camera Raw

Out Darn Noise

In Lightroom (and Adobe Camera Raw), there are two simple – and effective ways – to reduce noise in photos. One is to selectively reduce noise using the “Noise” slider of the adjustment brush as we saw with our selective saturation adjustment. Moving the Noise slider to the right increases the amount of noise reduction but does not give you control over what KIND of noise reduction is performed. ACR and Lightroom have specific controls to reduce Luminance noise (dark and light speckles) and Color or Chroma noise (colored speckles). The noise reduction slider with the Local Correction brush does not let you control which type of noise reduction is applied. Sometimes correcting only the luminance noise is the best approach.  Both methods of correcting noise result in some blurring of the photo. How much blurring depends on how severely the sliders are adjusted. There is no formula for getting noise reduction to work well except to be careful not to over do it!  Surprisingly, a little bit of noise makes a better photo. Indeed, there is an option to ADD noise in the “Effects” panel (called Grain). One thing to beware of: using the color noise reduction aggressively will result in loss of star colors in your night sky. In the examples below we’ve brightened the image to make the changes easier to see.

Before any Adjustment

Before any Adjustment, Turning off the default sharpness enhancement.

The noise reduction portion of Lightroom is found in the Details section. Any controls used in this section will apply to the entire image – which is one reason adjustments should be made carefully and deliberately.  The first step we usually take is to eliminate the default sharpening that Lightroom wants to apply.  We would rather selectively sharpen what needs sharpening than doing indiscriminate global sharpening. Next zoom in to 1:1 view of an area (Z key) where noise reduction is needed. For this pick a dark area where some details should be observable. It is also helpful to pick a dark area adjacent to a lighter area where sharpness is desired so the effect of noise reduction can be seen on two elements at once.

We slowly bump up the luminance until we see less “grit”.  Be sure to wait long enough to see the changes made in the image.  We do not generally notice much difference with the detail and contrast sliders, but if we find ourselves adjusting as far as halfway on luminance and not getting what we want, we play with those sub-sliders.

If we still have not achieved the correction we want, we bump the color slider as well… only much more carefully. If there is a LOT of color noise the color noise correction may be the slider to bump first.  Once things are “almost” where we think they look right we choose another area to take a look. It  is important to select an area of the photo that did not need much adjustment – usually a bright area. If the brighter area has become too blurred, we back off on the overall adjustment and then use a local adjustment to add still more noise reduction selectively.

All adjustments made - note that perfect smoothness is not a goal.

All adjustments made – note that perfect smoothness is not the goal!

In Photoshop there are many more ways to reduce noise than those provided in Lightroom and Adobe Camera Raw. Our experience is that the noise controls in ACR and Lightroom are very good – better than any specific filters you will find in Photoshop.  We do use Topaz Lab’s DeNoise photoshop plugin quite a lot however.  The best noise reduction method – when possible is to use the Simple Astrophotography Processing Technique. A photo like the one shown here is not a candidate, however, because that Astro technique requires multiple frames of the same image – that wasn’t possible here with the aircraft coming in for its bumpy landing.

Finished Image

Finished Image

In the next article we will take on the remaining subjects, but you may have already figured out one of the techniques we use for desharpening – aggressive noise reduction!

The Revenge of Lens Correction

There are plenty of ways to make your images look weird.  Some of the perturbations are due to sneaky little things that Adobe Photoshop, Adobe Lightroom and/or Adobe Camera RAW might be doing to the data.  We already talked about the “cooking” that is applied by default to RAW images and why letting that cooking stand unchallenged may be a bad thing. We’ve even warned you about Blur and Jaggies that may NOT in fact be in your images.

Recently Dan Barr asked us what we thought was causing a problem in his stacked star trails. If you read the title you’ve probably already figured out the culprit… Lens Correction!  You may not notice anything weird if you process only a single image, but what if Star Trails, or image stacking are what floats your boat?

Notice the strange pattern in the upper left. This image is cropped from a larger image.

Notice the strange pattern in the upper left. This image is cropped from a larger image. Image by Timbo2013

Look in the upper left of the image above. That cross hatching is one possible artifact.

Why does this happen? The lens correction is a mathematical model that moves pixels around. Not surprisingly, since the images change – even if slightly, the results vary slightly, too.

How do you fix the problem?  Don’t mess with your images before you stack them.  Save the lens correction, contrast adjustments and other tweaks for after you’ve finished stacking.

Here is a before and after comparison:

danBarr_moire_marked

Notice the odd “Moire” like pattern above and to the right of the mountain? (Image courtesy of Dan Barr)

It’s a little subtle. Here is the weird part close up – notice the vertical undulations? The oddness somewhat resembles sensor banding noise except when you look at a larger scale, the lines are concentric.

ConcentricLines

 

By redoing the stacking operation without performing lens correction, Dan was able to get an image without the waves:

Stacked first, then adjusted – no moire!

With the strangeness vanquished, Dan was able to improve the brightness and contrast as well.

Extraordinary Vision

Last month, one of Steven’s images was featured in the great (and free) Extraordinary Vision magazine issue 9 which is published online via iTunes.  

I_305-0347This month in Extraordinary Vision issue #10 you’ll find 17 pages of instructions on finding and photographing the Milky Way. If the article seems familiar, it is! The Extraordinary Vision article is an updated and combined version of our three-post series on the Milky Way:

If you haven’t checked out the magazine, please do – not just because of the Milky Way content, but because Angelo Ioanides does a fantastic job curating and writing great content. And it’s FREE – just like the content on our BLOG.

First page of the article.

I_305-0346

 

 

 

 

 

 


Click the image below to find the Extraordinary Vision Magazine:

Extraordinay Vision Magazine published on iTunes

 

 

 

 

What you Need to Know About Histograms

Original Publish Date: 11-September-2013
Last Revision: 11-April-2016

If you ask us what is the most potent tool a night photographer can wield, we’ll tell you: the histogram.  Unfortunately nearly all of the histogram information available seems to spend too much effort talking about what a histogram should look like and not enough time explaining what a histogram is… or that there are many different histograms and not all are equally useful. For example there are: luminosity histograms constructed from thumbnails created by the camera, there are color histograms created from the same thumbnails, and then there are luminosity and color histograms based on the actual sensor data – but those are rare. And of course there are still more histograms.

In the Beginning [41_03766]

In my early days I took what I thought was a fabulous photo of the fog creeping up against Mount Allison. It looked SO good on my LCD that I knew I was going to be in love with it. When I got home I realized that it was far from ideal. It was woefully underexposed and very noisy. That’s because I didn’t think to look at the histogram. At the time noise handling was not great and it wasn’t until recently that I was able to tease a half-decent image out of the data.

Deconstructing A Histogram

The best way to understand a histogram is to experiment. But before we launch into some experimentation, let me take a stab at explaining what a histogram is.

A histogram is a graph that shows the distribution of brightness (luminosity) over the range from the darkest possible to the brightest possible pixel.

Each vertical column reveals the number of pixels in the image that have that brightness level.  Usually the left edge is the darkest possible pixel – black – and the right edge is the brightest possible pixel – but there are some variations in histograms which we’ll cover in a moment.  Here is a degenerate, but perfectly valid histogram.

LuminosityHistogram_degenerate

A histogram showing only three brightness levels – nothing brighter than mid range.

In the graph above, we see that there are some (we don’t know how many) of the darkest possible luminosity.  A lot of pixels that are relatively dark – corresponding to the tallest line, and a few pixels in the “midrange” of possible brightness values at the next stubby little line. The image from which the histogram was made is this one:

BoringImage

A degenerate image composed of several primary colors.

Looking at the image, it is pretty obvious that the little bump on the far left of the histogram is the black frame around the border. The tall line in the histogram is the brown, and the little bump near the middle is the orange color.  What may be puzzling is that the orange looks pretty bright and you would not expect it to fall only about half way across the range from darkest to lightest values. In fact there IS clipping in the red channel – but we don’t see that in our luminosity histogram above! We’ll see why in a moment.

post-it-note-thIf one of the columns reaches the top of the graph it does NOT mean a “blow out” has happened. Likewise if a column appears at the right or left edge of the graph it does not mean that data has been lost or “blown out” – it does indicate that there MIGHT be a problem.

ColorHisto.bmp

At right is another histogram for the same data, 4 of them, actually. This was created using Photoshop’s “All Channels” view.

Rather than luminosity, the top histogram is in mode RGB showing each of the colors in this simple image against the maximum for that color.

The Red Histogram shows a complete range of reds from the darkest possible in the black border to the lightest possible. And here is where we first get a clue that quite a bit of the red is in the extreme right hand side of the histogram.  Most of the red is contained in the brown color.

The Green histogram shows the darkest possible green (i.e. black) and some green in the left 1/5th of the possible values, while the blue histogram shows only black and very dark blues.

Admittedly the image is not illustrative of a typical photograph of any kind. It does show clearly how the histogram corresponds to the values in the image.

HistogramLayersII.bmp

The image was created from additive layers using blend mode Linear Dodge ADD. Each layer has been constructed in different colors using the color value shown.  It is easy to calculate the majority of RGB triplets as: R=128, G=64, and B=32+16 (48).  The maximum values for an 8-bit image would be 255,255,255 – the value of the “whitest possible white” in this color space.

You might expect that the graph would reflect the 16-bitness of a 16 bit image. The maximum values for each color then would be 65,536, not 255 – but that’s not the way Adobe shows it.  A 65 thousand pixel-wide histogram would be beyond unwieldy.

Did you just have an “aha” moment?

One reason why the histogram is not completely trustworthy is that it is a composite of many luminosity values being lumped into one.  How so? Imagine possible values from 0 to 65,000 shown on a graph with only 255 different columns. A lot of “lumping things together” is present! It is possible to have lots of data in the darkest column and in the lightest column and still not have any blow outs or blacks.  Imagine it this way, suppose there were 100 possible luminosity values, but the graph showed just 20 columns.  The leftmost column would include values ranging from 0 to 4, the rightmost (brightest) would hold values from 95 to 99. So, in theory, you could have no zeroes and no maximums, but your graph would still show you data at each extreme.  This is where the histogram in Adobe Camera Raw is much more useful and accurate. Or to be more accurate, it’s not the ACR histogram that shows that much additional data, but it does indicate when items are being “clipped” – that is, reach the maximum or minimum.  But we’ll get to that in a minute.

In case you haven’t had another AHA moment, we want to explain why the histogram you see on your camera LCD should be regarded with suspicion.  That on-camera histogram is created from the thumbnail JPEG which is also shown on your LCD.  To go from raw data to a JPEG involves lots of operations including scaling 14 bits of information down to 8 bits, taking megapixels of resolution down to kilo-pixels and applying default curves and color assumptions. With that much data manipulation going on, your histogram reflects what your image MIGHT look like as a tiny JPEG and therefore may not accurately reflect what you’ve captured.

Get A Better Histogram: Use Lightroom or Adobe Camera Raw

Below is a much more useful histogram as found in Lightroom or Adobe Camera Raw. Your camera is not going to give you this level of detail- though you might have a highlight and/or shadows clipping indicator which we recommend you use.  ACR and Lightroom both offer shadow and highlight clipping indicators. Clipping indicators are enabled using the triangles in the upper left and right of the histogram or with the “J” key which toggles both indicators either on or off.  If you turn ON the clipping indicates by default blue dots replace clipped shadows and red dots reflect clipped (aka blown out) highlights.

First the wide view.

OverAndUnder_wide

Wide View: Shadow clipping (left arrow in histogram) turned on. BLUE indicates where the values have fallen to zero.

And here zoomed in:

OverAndUnder_zoomed

Zoomed in: We see both clipping in the shadows and highlight clipping (circled -though there are many more).

Notice how in the 1/4 view, we see much more shadow clipping! If we zoom in to 100% it will be even more obvious. As we adjust the exposure, shadows, contrast, highlights, blacks and whites the histogram and the clipping indications are reflected in real time.

Lightroom is also giving you a hint which colors are being clipped.  The blue triangle at the upper right tells you that the blue channel is causing the highlight clipping.  When multiple colors are being clipped, the triangle will include all the colors added together. Here the white triangle for the shadow clipping tells us ALL colors are zero – that is clipped in the shadows.  For this image moving the exposure to the right reveals that the green channel is the most clipped. 

Take Aways

  1. Don’t believe everything you see on the back of your camera display – especially not the image!
  2. Don’t only pay attention to the luminosity graph – it may hide highlight clipping in one or more colors. This is especially true, for example, if you take photos of red roses. You can cause the red channel to clip but the luminosity graph will look fine.
  3. Take a look at the color histogram if your camera has one.
  4. Just because you are taking photos at night doesn’t mean you can’t blow out the stars. Doing so means you’ll lose some color data, but it’s better to blow out a few stars than to have black for your foreground.
  5. Not every histogram (in fact most) won’t be “bell curves” – especially not at night.
  6. Oh, and please shoot in RAW. You keep a lot more of what you shoot that way!
C_208-66671

Clouds, Milky Way and Eerie Formations in Alabama Hills, CA

References