A technique I recently learned allows you to capture lots of dynamic range within photography with better results plus fewer downsides than conventional HDRs. I call it “AHDR” for “Averaged High Dynamic Range” photography.
AHDR isn’t a popular method at the moment, but I’ll create a case in this article for why I think should be. Compared to conventional HDR methods, it gives likewise good results but works much better when anything in your image is moving. It’s also just as easy and fast in order to capture as a normal HDR (actually a lot faster under several circumstances).
Also, feel free to call it whatever you want. I’m calling it AHDR in this article because I don’t want to type something like “the image averaging method of HDR” lots of times.
Table associated with Contents
What Is AHDR?
As the name Averaged High Dynamic Range suggests, AHDR involves averaging pictures together in order to get higher than usual dynamic range. I’ve written about image hitting before and how I utilize it to get high levels of fine detail in my astrophotography . Have a look at those articles if you haven’t already.
Prior to I show why I favor it over traditional HDR photography, let me first show how AHDR works.
Essentially, AHDR consists of image averaging, which makes use of the fact that most noise inside photography is random; it differs from photo to photo with no correlation. (Patterned noise is a different beast and obviously has patterns, but it’s minimal of all camera sensors. )
The noise in each image essentially “cancels out” when you average multiple photos together. The more pictures you average, the more it cancels out.
It’s easier to understand it when you see it. Here’s what sort of single image at ISO 6400 looks up close:
And here’s how it looks after I took 8 such photos in a line and averaged them within Photoshop:
You might be wondering how this has anything to do with HDR pictures. The answer is that the image hitting process substantially improves the photo’s dynamic range simply by shrinking the amount of shadow sound. The less shadow sound you have, the more details you are able to recover in the darker parts of a photo. The result – as with HDR – is that you can retain details throughout an image even in very high contrast moments.
Steps to make an AHDR
It’s very easy in order to capture an AHDR during a call. You simply take multiple identical photos of the scene ahead. (A tripod is highly suggested, as with regular HDR. ) Then, in post-processing software like Photoshop, you average the photos together. The resulting image has extraordinary levels of shadow detail that could be recovered using the standard sliders in Lightroom/Capture One/etc.
Something important to be aware is that AHDR does not improve highlight retention – just shadow detail. That may seem like a problem, but it really isn’t. This simply means that you must avoid broken highlights in your images at all costs, even if it means exposing darker than your meter recommends. Here’s another way to think about it: a conventional HDR involves (at least) a “centered” exposure, an “under” exposure, and a good “over” exposure. By comparison, the particular AHDR method involves taking “under” exposure multiple times.
Here’s an example of exactly how it looks in practice:
One minor drawback with AHDR is the fact that not all post-processing software has a way to average multiple photos together. You need to have specialized software like Photoshop or Appreciation Photo in order to do so.
The method of picture averaging is different in every software program. In Photoshop, one way to get it done is to load all your photos as layers, convert these to a single smart object (highlight all layers > best click > Convert to Smart Object), then visit Layer > Smart Items > Stack Mode > Mean. If you don’t know how to take action similar in your preferred software program, just search for a tutorial on-line.
How Many Photos Does a good AHDR Need?
While you may be worried that AHDR requires several photos in order to improve shadow detail, that’s not necessarily the case. In fact , every time that you double the number of photos you catch, you improve the shadow detail by one stop (i. e. making it twice as good; half as much noise).
A traditional HDR photo consists of taking at least three images: one at the metered exposure, one that’s overexposed by a stop, and one that’s underexposed by a stop. The result is a two-stop improvement in dynamic range over what your camera sensor can typically capture.
In contrast, an AHDR photo demands that you take four images for a similar improvement. You need to take the underexposed photo four times in a row, standard the four photos in post-production, and recover shadow details with your preferred modifying software.
That is what it takes for two stops of shadow improvement. If you want to recuperate three stops of shadow details, you would need to take 8 photos with the AHDR method. To recover four stops of shadow details, it requires sixteen AHDR photos. And so on, duplicity each time.
This is the biggest drawback of AHDR – the large number of photos you would need to take in order to recover more than about four stops of shadow details. Of course , very few real-world situations require so much shadow recovery. Most cameras have a base ISO of 100; four stops of shadow detail recovery is certainly equivalent in dynamic range to a base ISO of 6.
In case you have to simulate even decrease values like ISO 3, 1 . 5, or decrease for whatever purpose, a conventional 7-image or 9-image HDR is the route I recommend, instead of taking dozens of photos to average together. It’s just a little less hassle.
Proof of Similar Results Among HDR and AHDR
I’m sure that some photographers reading this are skeptical that the results of a good AHDR image are similar to the outcomes of a traditional HDR. So before I get into the advantages of AHDR, let me first demonstrate that the two methods are interchangeable in image quality under typical conditions.
Here’s a single image of a high-contrast construction tube. I chose to expose for your highlights at the center, which resulted in very dark shadows:
To capture detail in both the highlights and the shadows, you can create a three-image HDR as I described above: a -1. 0 image for the illustrates, a 0. 0 picture for the midtones, and a plus one. 0 image for the shadows. Here’s how that appears when combined:
Similarly, I can follow the AHDR process: Take four -1. 0 images, average all of them together, and recover the particular shadows in Lightroom. Here is how that result appears:
At these sizes, it seems to have just as much detail as the HDR. Let us look at some crops. Here’s a 100% crop of the single image, with the shadows brightened to match the other shots:
Lots of noise. Here’s the same crop from the HDR:
And the same crop in the AHDR:
As you can see, both the HDR and AHDR have far better noise performance than the one image. The noise extremes are equivalent in both pictures with no reason to prefer either one. In short, the HDR and AHDR images are usually interchangeable, and could be made to look basically identical with a bit of editing.
Still, if AHDR requires one more photo in order to get the same results as an ordinary HDR, the reason why in the world do I consider it the greater method? That’s what I’ll go over next.
Benefits of AHDR
Now that you’ve seen how HDR plus AHDR can produce similar results, let’s go over the benefits of AHDR. It is all about fixing the biggest negatives of regular HDR photos, which are as follows:
- HDRs tend to create ghosting artifacts when something in your photo moves.
- They result in unequal patterns of noise in the photo.
- They can lead to harsh, garish colors if not done carefully.
- They can take some time in order to capture in dark problems; individual exposures may be something similar to 15 seconds, 30 seconds, and 60 seconds, so you could be patiently waiting for a while.
AHDR improves upon those downsides. Let’s go through every point individually.
1 . Ghosting Artifacts
It is well known that HDRs frequently don’t do well when anything at all in your photo is relocating. This includes small details like tree leaves rustling within the breeze, as well as large subjects like ocean waves that could cover the entire foreground of your photo.
Sometimes, you can fix ghosting artifacts manually in Photoshop with all the spot-heal brush or by means of careful (often manual) image blending in the first place. Other times, especially with ghosting or “afterimages” along the fringes of your issue, they can be nearly impossible to remove.
Here’s an example of ghosting artifacts with HDR (100% crop from the full image). Zoom in if you’re on the phone, and click to find out full size if you’re on the desktop, if you can’t notice them right away:
This happened because the palm leaves were blowing in the blowing wind, and it’s hardly a good uncommon sight in HDRs. The anti-ghosting option in most HDR software isn’t a great solution either, since it generates issues of its own (especially uneven noise, as I can cover next).
How does the AHDR technique look by comparison? Here’s a similar 100% crop:
Much better! Some of the hand leaves have a bit of obnubilate now, but the effect is much less distracting to my attention compared to the HDR. If you’re worried, you can take a few more photos (this was only four) as well as your result will be smoother, basically mimicking a long exposure.
2 . Uneven Noise
Not as often discussed, and big of a problem, is the fact that HDRs can have uneven styles of noise after you’ve blended them together.
Here’s an example of a good HDR I created within Adobe Lightroom (whose HDR software tends to give realistic results but has some issues with noise patterns). The image appearance good at this size:
But cruising in, you can see that there’s a strange band of noise going across the sky:
That’s because Lightroom tried to merge part of the sky from your underexposed shot with part from the standard exposure, and yes it didn’t do a great job. This is simply not an uncommon result in a lots of HDR software, particularly in Lightroom if you have the anti-ghosting feature turned on.
(In case you were questioning, this result isn’t because I had different ISO ideals for each shot; all three of the images were taken at my base ISO associated with 100. )
I’ve gotten similarly strange results where Lightroom or even other HDR software misinterprets a moving subject when it tries to blend images jointly. Look at the strange clouds at the top right of this shot:
They didn’t look like that inside real life! They’re purely an invention of Lightroom. Once i noticed the issue, I had to blend the images by hand in Photoshop to get the correct result:
AHDR does not have these issues. Every image that forms an AHDR has the same exposure settings together another, and you’re simply doing a simple average in order to blend them. As such, there’s no room for unequal noise or misinterpreted content to sneak in.
3. Garish Colors
Most photographers who rely on HDR already have a favored way to avoid garish colors. Nevertheless , it remains the case that the majority of HDR software gives outrageous results by default, such as the “Merge to HDR Pro” function built into Photoshop.
Whilst I’ve had good luck avoiding such exaggerated tones in Lightroom’s HDR merge feature, Lightroom also has the most issues with uneven noise of any HDR software I’ve attempted. So , you can’t necessarily fix the garish color problem just by switching software.
A common solution is to utilize luminosity masking in order to draw the best parts from each image and merge all of them together. This is indeed a very good way to blend different exposures together and I have no issues with it at all, other than time it takes to do manually if you don’t have a Photoshop plugin like Lumenzia or TK Lum-Mask .
The AHDR method furthermore gives stellar results without having garish colors. After you have merged an AHDR picture, it looks essentially the same as each photo that makes it up – i. e., exactly like any ordinary raw file. It also functions just like any ordinary raw file, other than it has drastically better darkness recovery than usual.
4. Timeframe to Capture
Since AHDR usually involves taking more pictures than a standard HDR, you may be thinking that it’s slower plus takes more time in the field. Yet that’s not really true.
For one, taking a break open of four or eight photos in a row is extremely quick and easy on most cameras these days. You can be finished with the whole AHDR in a matter of seconds. However , the same can be said of the traditional HDR if you enable bracketing beforehand, so this can be quite much a tie.
The real speed advantage of AHDR is when you’re shooting in darker conditions, where you need multi-second exposures in order to capture enough lighting. Take this scene, for example:
I actually took this at 60 seconds, which was right at the meter’s recommendation. Thankfully, this picture didn’t have enough dynamic range to require an HDR. If it did, I’d have needed two additional exposures: one at 30 just a few seconds and one at 120 secs. Add those together, and I’d have been waiting around with regard to 3. 5 minutes while our camera captured the HDR.
By comparison, an entire AHDR shoot would be required for 2 minutes! That’s four individual photos of a 30 second shutter speed each. The result would have as much powerful range as an HDR, yet would take about half you a chance to capture. Even ignoring the rest of the benefits of AHDR, I definitely recommend using it in lower light to save yourself period.
Of course , this only applies if you’re firing HDRs in very dim light. In regular conditions, taking either an HDR or an AHDR will be very quick regardless.
Other Things to Note
one Mean vs Median
Any time I actually write about image averaging, whether for astrophotography or regarding improving the image quality of a drone , I get the same question: Do I actually recommend averaging the photos together? Or do I actually use the median blend option in Photoshop instead?
Photoshop has a setting for both mean and median, and also a host of other picture blending modes. To explain, I always use mean, not median. Every time that I second-guess myself on that, I actually go back and test once again, and I always see that mean to say has a bit less noise. I have yet to work with a collection of images where median really does a better job reducing noise.
Somewhere online there must be someone saying that median could be the way to go, because I have this question a lot. I just encourage you to do your own testing to see for yourself. The differences are usually small, but mean looks better.
2 . Why Bother Taking Multiple Exposures?
Another, more entertaining question that I get amazingly often is this: Why can’t you just take one photo, duplicate the layer a bunch of times in Photoshop, then average that result rather?
The reason is that will averaging a dozen copies – or a hundred, or a million copies – of a solitary photo will only ever enable you to get back to that single first photo. By comparison, the AHDR method works because the sound patterns change across multiple images, while the “subject styles, ” so to speak, stay the same.
I like the out-of-the-box thinking, but there’s no way around it; you need to consider multiple photos in the field, or AHDR doesn’t work.
3. One last Benefit
The last thing I’ll say about AHDR is that it has yet another nice benefit: minimal reduction in image quality in the event that one photo in your sequence doesn’t turn out right.
With regular HDRs, a single accidentally blurry shot (perhaps you bumped your own tripod during the “under” photo without realizing it) can make it difficult or impossible to merge the images properly later. On the other hand, with an AHDR, just delete the blurry photo and blend the others. You’ll lose a slight bit of shadow recovery since you’re not averaging as numerous shots, but nothing major.
I hope this method gave you some ideas, and maybe you’ll find that the AHDR method (or whatever you want to call it) is useful for your own personel photography. As much as I prefer “getting it right in-camera, ” having a technique in your back pocket for tricky, high-contrast situations is always a good idea.
Of course , just because the AHDR method has some great benefits doesn’t mean that you’ve been doing anything wrong if you’ve shot regular HDRs over the years. I only figured out this technique a few months ago myself, and while I’m going to use it rather than HDR from now on, traditional HDR photography is hardly bad. When nothing in your photograph is moving, the two methods will generally give you the same results – and AHDR isn’t a good substitute for 7-image or 9-image HDRs that capture truly massive powerful range.
When something in your photo is certainly moving, even if it’s just a few tree leaves in the length, I recommend trying out the AHDR method to see if you like the final results. It’s easy and quick, and it repairs most of HDR’s issues with out bringing any major troubles of its own. Hard to request more than that.