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Tony Northrup is wrong (about Adobe Super Resolution)

Take a look at the left and right halves of the image below. Hover with your cursor and click.  These come from the same Canon RAW image. The left half is unprocessed, the right has only been upscaled using Adobe’s Super Resolution. The difference is clear.  Tony Northrup’s YouTube video on Super Resolution was sent to me independently by two members of a local photography club.  In it, he claims that Super Resolution is useless on all but Fuji X-trans files.

 

He’s wrong.  He misses the  primary use of the feature, namely, for shots that are heavily cropped. This photo is such an example. Ignore that it is boring; I grabbed it from an online sample.

Understand that the difference between the left and right images is hardly an anomaly. You will get similar results with any reasonably sharp low-pixel image.  The Super Resolution feature would yield similar results for a fuller frame image that had to be blown up to a very large size, such as a wall mural.

Northrup’s conclusions only apply to the case he presented: a well-composed full frame image displayed at moderate size.  (Even so, he compared an unprocessed Super Resolution image with an image he further tweaked for detail – not quite fair.) 

So for the images he worked with, he is correct that improvements are too marginal to be worth the effort.  Perhaps Northrup has no shots that suffer the low-resolution blues due to heavy cropping.

I am not so lucky.  So I used Jeffrey Friedl’s Data Explorer, a crazy useful plugin (grab it and tip him a few bucks) that allows Lightroom to find and group images by more than 200 data criteria – criteria like crop-amount.

I found dozens of images cropped at a rate of 50% or more that easily become candidates for Super Res treatment! These images become “rescue” images, and I hope that in the near future I’ll be able to batch-process them in Lightroom’s super resolution implementation (soon please, Adobe.)

Side note:

Fuji X-trans RAW files represent a special case; they require a specialized processing, and Lightoom’s less-than-stellar treatment has often led photographers to seek third-party solutions.  Some of these X-trans images will benefit from Super Resolution even at more “normal” sizings.

See my original post on Super Resolution which also has other image samples.

Below, a rather extreme blow-up.  

[twenty20 img1=”24032″ img2=”24031″ offset=”0.5″ before=”without” after=”with super resolution”]

LImagine these two treatments represented two different lenses. Would you want to take one back?

[twenty20 img1=”24095″ img2=”24096″ offset=”0.5″]

The original photo, to illustrate size. To reiterate, super resolution won’t make a difference unless you’re blowing an image up to a very large size, or using a very severe crop.  In either of these cases it can make a large difference.

Take a look at the left and right halves of the image below. Hover with your cursor and click.  These come from the same Canon RAW image. The left half is unprocessed, the right has only been upscaled using Adobe’s Super Resolution. The difference is clear.  Tony Northrup’s YouTube video on Super Resolution was sent to me independently by two members of a local photography club.  In it, he claims that Super Resolution is useless on all but Fuji X-trans files.

He’s wrong.  He misses the  primary use of the feature, namely, for shots that are heavily cropped. This photo is such an example. Ignore that it is boring; I grabbed it from an online sample.

Understand that the difference between the left and right images is hardly an anomaly. You will get similar results with any reasonably sharp low-pixel image.  The Super Resolution feature would yield similar results for a fuller frame image that had to be blown up to a very large size, such as a wall mural.

Northrup’s conclusions only apply to the case he presented: a well-composed full frame image displayed at moderate size.  (Even so, he compared an unprocessed Super Resolution image with an image he further tweaked for detail – not quite fair.) 

So for the images he worked with, he is correct that improvements are too marginal to be worth the effort.  Perhaps Northrup has no shots that suffer the low-resolution blues due to heavy cropping.

I am not so lucky.  So I used Jeffrey Friedl’s Data Explorer, a crazy useful plugin (grab it and tip him a few bucks) that allows Lightroom to find and group images by more than 200 data criteria – criteria like crop-amount.

I found dozens of images cropped at a rate of 50% or more that easily become candidates for Super Res treatment! These images become “rescue” images, and I hope that in the near future I’ll be able to batch-process them in Lightroom’s super resolution implementation (soon please, Adobe.)

Side note:

Fuji X-trans RAW files represent a special case; they require a specialized processing, and Lightoom’s less-than-stellar treatment has often led photographers to seek third-party solutions.  Some of these X-trans images will benefit from Super Resolution even at more “normal” sizings.

See my original post on Super Resolution which also has other image samples.

Below, a rather extreme blow-up.  

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Adobe Super Resolution – Fuji X-trans Game Changer!

Impatience got the best of me so I didn’t wait for Adobe’s new Super Resolution feature to reach Lightroom (it’s said to be coming soon).  So I tried it in Photoshop’s Camera Raw. Let’s cut to the chase – the results in certain circumstances are nothing less than staggering.

The following images tell the tale.  The one on the right is the Super Resolution image with four times the number of pixels as the original.

Note that this example is extremely blown up to 200% for the comparison.  At normal viewing levels, the differences aren’t nearly as impressive (more on this later).

Fuji shooters know that certain features such as leafy vegetation haven’t done so well with Adobe’s demosaicing algorithm. Fuji’s X-trans sensor uses a non-standard photosite array that while resolving some issues, has not had the greatest results with non-specialized (read: Adobe, for one) RAW sharpeners.

Tech note:

The easiest way to run it currently is by opening your RAW image (or jpg, but why?) in Photoshop (set to open files in Adobe Camera RAW mode). It’s hidden under the three dots and “enhance image.”

The massive file produced is autosaved to the original directory. You need import it into Lightroom.  I’ve had intermittent app crashes, and so far the best results seem to come when I close Lightroom and open the target file after Photoshop is already loaded, but have seen no clear reporting of this at the Adobe site. Your mileage may vary.

The below image represents a 200% blow-up.

[twenty20 img1=”23850″ img2=”23851″ offset=”0.5″ before=”Fuji RAW” after=”Super Resolution” hover=”true”]

A picture does say a thousand words, doesn’t it?  This was shot on my Fuji X-E2 which has 16 megapixels. It might forestall my need to upgrade in the neverending chase for more pixels.  I don’t know if images from cameras using traditional Beyer sensors will see as marked improvement.

Tony Northrup in a youTube video Photoshop Super Resolution: 4X megapixels (actually tested-surprising!) reports that the enhancement offers little improvement for non-Fuji images.  Tony is wrong by being right only in a limited sense:  Wrong About Super Resolution.

How does Adobe do this magic?  You’ve probably been hearing a lot more about artificial intelligence (AI) recently.  From Adobe’s website: “The idea is to train a computer using a large set of example photos. Specifically, we used millions of pairs of low-resolution and high-resolution image patches so that the computer can figure out how to upsize low-resolution images.”

Prior to AI, achieving higher resolution was done by blowing an image up to double its dimensions and then using a mathematical algorithm (bicubic interpolation) which essentially smooths the image by giving each pixel a bit of information from its neighboring pixels.  (Imagine each pixel as the center of a tic-tac-toe board, “borrowing” a little bit of information from each of its eight neighbors.)

With AI, something very different is happening; new information is added based on what the software thinks (from massive trained experience) should be there!

It should be understood that by creating pixels out of whole cloth, so to speak, AI can create problems of its own.  The information supplied might not be right. Artifacts can be introduced.

Below: the same image at 100%.  Notice how at this resolution, differences are minimal. Pay close attention to the bricks, directly under the glass portion of the light, the bare branches to the right of the light, and the bare branch that parallels the light. Both detail and color are improved, but ony marginally.

[twenty20 img1=”23883″ img2=”23882″ offset=”0.5″ before=”Fuji RAW” after=”Super Resolution” hover=”true”]

What’s the takeaway here?  If you’ve captured a scene full-frame and it is displayed at a normal size on, say the internet, or a 4×5 sized print – the difference will be visible, but very marginal.  But say you’re blowing up the image to an 8×11 or much larger print – then the difference can be very visible.

Let’s take a different example: you’ve taken a picture but discover in post-processing that you want to crop heavily.  Or perhaps you would have rather used a telephoto lens, but didn’t have one with you.  Blowing up your image would normally have shown extreme degradation.

Stephen Bay has done a super comparison of Super Resolution to Gigapixel AI, a product of Topaz software. Both products do essentially the same thing, with similar results.  I might prefer the Gigapixel treatment slightly; I like the denoising they add, not see it as fakey as Stephen does and am not bothered as much by the artifacts.

But these are quibbles; both products create magic.  It should be noted that both products create new image files that are much larger than the original RAWs. My Fuji shots are around 33mb in size and Super Resolution adds a new file about eight times larger!  In other words, this is a process best reserved for truly deserving shots.

The Topaz product, according to Bay, takes several minutes to process an image.  the damage for Adobe’s isn’t nearly as great; it took under a minute and a half for my XE-2 RAW on a mediocre computer.

Imagine these two treatments represented two different lenses. Would you want to take one back?

[twenty20 img1=”24095″ img2=”24096″ offset=”0.5″]

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Sharpen AI by Topaz Labs – a Winner!

Not long ago, I had the opportunity to photograph Kiane, a lovely Minneapolis-based model. One of my favorite shots of her was spoiled because I missed the focus. (Note to self: avoid using manual focus lenses in situations that are risky.)

Enter Sharpen AI, a software product created by Topaz Labs. Their claim was sharpening “repair jobs” that border on the miraculous, so I thought I’d give them a try. I’ll cut to the chase and present the before and after. The left ‘before” side represents my best effort to sharpen the image in Lightroom, the right “after” image is with Sharpen AI by Topaz Labs.

I have seen some “knock your socks off” examples, but this is not one of them. On a mobile device you won’t be able to see the difference, but on a desktop computer, particularly around her eyes and mouth, the difference is obvious. And it is exactly the difference between a shot that doesn’t quite make it, and one that does.

A side note or two. The image was shot on a Fuji X-E2. Results with Sharpen AI seemed to be better if I did not try to sharpen in Lightroom first, but this should be considered an early result. Also, Sharpen AI has three different sharpening “specialty” modes and I would not have considered the softness in this image, (exposed at 1/250s) to be a result of motion blur. But Sharpen AI’s auto-detect said that stabilization mode was the best way to go, and indeed it was. 

[twenty20 img1=”22753″ img2=”22757″ offset=”0.5″ before=”Before” after=”After”]

On the right is another example. Viewed from a desktop computer, the difference might be marginally noticeable, and on a phone is invisible. Now click on the image for a blow-up. The difference is obvious. (The left image is a little over-sharpened – I didn’t take the time to fix it.)  This helps illustrate an important point: sharpness is largely dependent on resolution, which is a function of viewing size and/or viewing distance.

This principle illustrates the “danger” of pixel-peeping; you can waste a lot of time and effort working to sharpen an image to use at a size or (less often) distance that renders the additional sharpening unnoticeable. Learning when and where it matters is key.

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A quick note on my posts…

Most of these posts are in a state of creation and/or repair. Just as I’ll tweak images over time, so too, with my blog posts – with one important difference: while I’m not likely to post an image I think ‘deserving’, it’s a different story with most of my posts. Some of them barely deserve to be called rough drafts. The blog area is my mental sketchpad for ideas, and I’ve elected to just post them, unfinished.

I might even add more to this post, too.

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“Sharpness is a bourgeois concept.”

Henri Cartier-Bresson and the Decisive Moment.

“He had his little Leica,” [fashion photographer Helmut] Newton remembers, “and he simply would point and shoot.” Since Cartier-Bresson’s hand isn’t as steady as it used to be, some of the pictures were a bit fuzzy. “Sharpness,” he told Newton, “is a bourgeois concept.” Newton sits back and laughs: “I thought that was just divine.”

– Dana Thomas, Newsweek, 6/1/03

© Henri Cartier-Bresson/Magnum Photos // US photographer, Helmut Newton posing at the Chopin monument, at the Parc Monceau in Paris. 2000.

Cartier-Bresson’s tongue-in-cheek condemnation of clarity (a better translation of the French “netteté”) can be viewed on several levels.  While joking about his shaky hand, his overwrought language satirized the high-dudgeoned political correctness that even then informed art criticism.

But as Mark Twain once called humor “the good-natured side of a truth,” in some sense Cartier-Bresson was also affirming his belief that clinical sharpness was in fact, an overrated photographic virtue.

Of course, neither did he consider softness a virtue — he simply did not value clarity for clarity’s sake. His credo might be summarized: “an image needs the clarity it needs, and no more.”

The title of his famed photographic collection, The Decisive Moment, says it all: for him photographic resolution was the resolution of time rather than print resolution. The moment and composition trumped photographic clarity.

Three Boys at Lake Tanganyika, Martin Munkácsi c. 1930

Cartier-Bresson wrote that after seeing this photograph “I suddenly understood that photography can fix eternity in a moment.”

Different types of photography have different holy grails. Compare Cartier-Bresson’s desire to “fix eternity in a moment” with Ansel Adams’ goal: tack sharp with every tonal value correctly pre-envisioned.

Adams carefully planned exposure and film development of each shot using his zone system; Cartier-Bresson didn’t process his own images, but simply gave his film to a trusted processor, and rarely did any cropping from his full frame shot.


It never occurred to me until later that in order to take that picture, Capa had to get ahead of that soldier and turn his back on the action.
– John Morris, Capa’s editor at LIFE magazine

Invasion of Normandy Beach, Robert Capa
Invasion of Normandy Beach, Robert Capa


“We cannot develop and print a memory”

“Of all the means of expression, photography is the only one that fixes forever the precise and transitory instant. We photographers deal in things that are continually vanishing, and when they have vanished, there is no contrivance on earth that can make them come back again.

Behind the Gare de Saint-Lazare, Paris, France. 1932 © Henri Cartier-Bresson/Magnum Photos

We cannot develop and print a memory. The writer has time to reflect. He can accept and reject, accept again; and before committing his thoughts to paper he is able to tie the several relevant elements together. There is also a period when his brain “forgets,” and his subconscious works on classifying his thoughts. But for photographers, what has gone is gone forever.”

-Henri Cartier-Bresson: The Mind’s Eye: Writings on Photography and Photographers