[HN Gopher] Adobe Photoshop's 'super resolution' made my jaw hit...
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Adobe Photoshop's 'super resolution' made my jaw hit the floor
Author : giuliomagnifico
Score : 143 points
Date : 2021-03-13 19:03 UTC (3 hours ago)
(HTM) web link (petapixel.com)
(TXT) w3m dump (petapixel.com)
| frereubu wrote:
| Pixelmator did this a while ago (December 2019):
| https://www.pixelmator.com/blog/2019/12/17/all-about-the-new...
| Can't comment on the quality of either because I haven't used
| them, but they both seem to go to 200%.
| nerbert wrote:
| I use it regularly (for my personal photos) and it depends on
| the photo. My observation is that pictures of natural elements
| (clouds, water, stars) tend to yield better results than for
| example a family picture in my house.
| marcodiego wrote:
| The samples on the article show very good results at preserving
| details so that curves do not get blurry when scaled up but are
| not particularly impressive.
|
| Off topic: I remember a few years ago, some students got very
| impressed by GIMP's Lanczos-3 upscaling that was much better than
| the photoshop version they had access at the time.
| PaulHoule wrote:
| The 'Preserve Details 2.0' upscaler from photoshop does an
| amazingly good job, in particularly I started with a 500x500
| square image of a gundam sketch illustration which showed scan
| lines when printed directly on a 8 inch square but with 4x4
| scaling the image was close to perfect.
| uniqueid wrote:
| I thought it was interesting when Pixelmator introduced 'ML Super
| Resolution' but that was in 2019.
| https://9to5mac.com/2019/12/17/pixelmator-pro-super-resoluti...
| [deleted]
| jl6 wrote:
| Is it just me who found the article immensely frustrating for
| lacking same-region-crop side by side comparisons?
| ghshephard wrote:
| I don't even understand why someone would use a headline like
| "Jaw hit the floor" without even bothering to share the two
| images. It's not like Adobe Photoshop doesn't have the ability
| to export images...
| lupire wrote:
| Adobe paid to promote their stuff
| rrrrrrrrrrrryan wrote:
| No, it's not just you. An overlay with a slider is pretty much
| the standard way of comparing two near-identical images these
| days, but this article not even having a side-by-side is just
| downright lazy.
| 0-_-0 wrote:
| Exactly, an article about superresolution doesn't atually show
| what superresolution is doing, despite the terribly clickbait-y
| headline.
| [deleted]
| Waterluvian wrote:
| Does the ML algorithm with the millions of images training set
| get run locally or as a remote service?
|
| When I use ML these days is it all hardcore data crunching by
| remote servers or is some of it running on my phone/laptop?
| Invictus0 wrote:
| The model is trained on Adobe servers and run locally on your
| device. The training of the model is much more processor
| intensive than actually utilizing the trained model, usually by
| multiple orders of magnitude.
| Waterluvian wrote:
| Ah okay thanks.
|
| So any model that can be trained for generic use (Eg. Not
| trying to deep fake my specific face) can presumably be run
| on local machines.
|
| Thanks!
| robk wrote:
| Topaz Gigapixel is quite good though despite the negative
| comments in the article. I'd rather give my money to Topaz for
| this one feature than keep paying Adobe subscription fees
| indefinitely
| AmVess wrote:
| I've been using it for a year. Each version gets a little
| better than the last and it gets regular updates.
|
| It also has the rather large benefit of not being tied to
| Creative Cloud, which is one of the worst bits of software ever
| made.
| CharlesW wrote:
| Because they're David to Adobe's Goliath, I also feel compelled
| to mention that I've just recently discovered/purchased this
| and am incredibly impressed with it.
| afavour wrote:
| I guess professional photos have always been touched up and this
| is just an automation of this process. But I've long felt a
| little odd about the use of machine learning in photography this
| way. How long before Google Photos recreates dark photos by just
| reassembling all of the items it believes are there from machine
| learning?
| medlyyy wrote:
| I mean, the only reason things like this aren't commonplace is
| cost (of the skillset, tools). Basically anything's possible
| these days with CGI, everything is purely a matter of the
| amount of effort you want to put in.
|
| And for artistic purposes, why does it matter how the final
| result was arrived at? If we have powerful and easy techniques
| for realising an artistic vision, that doesn't seem like a bad
| thing?
| kristiandupont wrote:
| ...I guess the _enhance_ jokes were just rendered void!
|
| https://www.youtube.com/watch?v=Vxq9yj2pVWk
| [deleted]
| endisneigh wrote:
| I'm not sure if that's the case with this tech. I could see in
| the near future a scenario in which many, many individuals
| (thousands) are photographing the same things in the same area
| and you can intelligently superimpose things to "enhance", tho.
| benlumen wrote:
| CSI and the like were just ahead of their time
|
| edit: I was joking, but people pointing out that you still
| can't create something out of nothing etc might not be thinking
| big enough. I think this technology absolutely has the
| potential to help. police are literally still using artists
| impressions - photofits, to find perpetrators
| hojjat12000 wrote:
| I think the artist impression has a lot more value than a
| highly realistic generated face. If you see an artistic
| impression, you will see the facial features that were
| noticeable. Such as a mole, the shape of the nose, or the
| thickness of the eyebrows. Then you have a template that your
| brain uses to match those features with any face that you
| see. However, if I show you a highly realistic face, your
| brain will take a different impression. Your brain is trained
| on faces for thousands of years. It will try to match the
| face perfectly.
|
| An artist impression tells the audience that it is
| inaccurate. A realistic photo tells the audience that this is
| _exactly_ who we are looking for.
| medlyyy wrote:
| Yep. To be useful for exploring potential "true" values, a
| system would probably need some way of showing you the
| distribution of its guesses, so you can get an idea of
| whether there is any significant information there.
|
| That aside, you'd still probably need a ML PhD to have a
| chance of correctly interpreting the results, given the
| myriad potential issues with current systems.
| tyingq wrote:
| This demo is fun to see the ML take different guesses at how to
| inpaint the missing data:
| http://www.chuanxiaz.com/project/pluralistic/
| qayxc wrote:
| Absolutely not. If there's not enough information available,
| there's not enough information, full stop.
|
| Plausible (i.e. "good looking" or "believable") results are not
| the same as actual data, which is why _enhance_ wouldn 't work
| on vehicle licence plates or faces for example.
|
| Sure, the result might be a plausible looking face or text, but
| it's still not a valid representation of what was originally
| captured. That's the danger with using such methods for
| extracting actual information - it looks fine and is suitable
| for decorative purposes, but nothing else.
| nwienert wrote:
| No there certainly is a chance for ML to improve here.
|
| Let's take the classic example of enhancing a blurry photo to
| get a license plate.
|
| Humans may not be able to see much in the blur, but an AI
| trained on many different highly down-res'd images could at
| least give you plausible outcomes using far less data than a
| human brain would be able to say anything with confidence.
|
| You wouldn't hold it up as the absolute truth, but you'd run
| the potential plate and see if it matched some other data you
| have.
|
| So yes, it wouldn't magically add any more information to the
| image, but it could be far better at taking low information
| and giving plausible outcomes that are then necessary to
| verify.
| qayxc wrote:
| > Let's take the classic example of enhancing a blurry
| photo to get a license plate.
|
| That's not the same as fabricating information, though. A
| blurry image still contains a whole bunch of information
| and correlation data that just isn't present in a handful
| of pixels.
|
| This is not super-resolution, but something different
| entirely. Super-resolution would mean to produce a readable
| license plate from just a handful of pixels. That is an
| impossible task, since the pixels alone would necessarily
| match more than one plate.
|
| The algorithm would therefore have to "guess" and the
| result will match something that is has been trained on
| (read: plausible), but by no means the correct one, no
| matter how many checks you run on a database.
|
| To illustrate the point, I took an image of a random
| license plate, and scaled it down to 12x6 pixels. 4x super-
| resolution would bring it to 48x24 pixels and should
| produce perfectly readable results.
|
| Here's how it looks (original, down-scaled to 48x24, and
| down-scaled to 12x6 pixels):
| https://pasteboard.co/JSu3WDU.png
|
| The 48x24 pixel version could easily be upscaled to even
| make the state perfectly readable. A 4x super-resolution
| upscale of the 12x6 version, however, would be doomed to
| fail no matter what.
|
| That's what I'm getting at.
|
| Just for shits and giggles, here's the AI 4x super-
| resolution result: https://pasteboard.co/JSu7jkP.png
|
| Edit: while I'm having fun with super-resolution, here's
| the upscaled result from the 48x24 pixel version:
| https://pasteboard.co/JSu9Qh6.png
| hojjat12000 wrote:
| Actually the opposite. These algorithms are more
| susceptible to noise, they may generate sharp perfect
| license plate numbers (that are totally fabricated and
| completely wrong) from a blurry image. But by no means
| should you even consider the results to have hints of
| truth.
|
| GAN produces totally different results if you slightly
| change the input.
|
| So, as others are also saying, these "enhances" are great
| for decoration and absolutely should be ignored as facts or
| truth (specially when it comes to face and license plate
| and others used by the law enforcement).
| nwienert wrote:
| No. If their training set isn't too far off what you use
| it for, it is valid. Just because it's not guaranteed,
| doesn't mean it's not more accurate than hitting sharpen
| and squinting.
|
| You're fighting against "would it be reliable" but that
| isn't the claim.
|
| The claim is could it be better than human, and the
| answer is yes, it just depends on how well trained it is
| and the dataset.
|
| But this is also entirely testable. I guarantee much like
| Go, if we set up a "human vs AI guess the blurry image"
| competition that AI will blow us out of the water. It's
| simply a data * training issue, and humans don't spend
| hours on end practicing enhancing images like they do
| playing Chess.
|
| Again - it won't be perfect, obviously. It will have
| false positives, of course.
|
| Doesn't mean it can't be better than human.
|
| Also GANs are pretty irrelevant, the model structure has
| nothing to do with the theory.
| perl4ever wrote:
| >These algorithms are more susceptible to noise, they may
| generate sharp perfect license plate numbers (that are
| totally fabricated and completely wrong) from a blurry
| image.
|
| This is not really an issue that is new or limited to
| things that are called AI.
|
| https://www.theregister.com/2013/08/06/xerox_copier_flaw_
| mea...
| medlyyy wrote:
| That's a fair point. Police use artist sketches from
| witness descriptions to help identify a suspect. It's a
| similar idea.
|
| The difficulty will be making sure people treat it the same
| way, because it _looks_ like a normal image.
| qayxc wrote:
| Hm. So I took the example image, upscaled by 200%, applied a
| sharpen filter (all in Paint.NET) and compared the result to the
| AI upscaled image. TBH, I couldn't see a difference.
|
| 2x upscaling isn't all that impressive to begin with (e.g.
| produce 4 pixels from 1) and can be done in fairly high quality
| using traditional non-learning algorithms.
|
| I'm much more impressed by 4x and 8x super-resolution. I'm really
| not sure what the big deal is with 2x.
| FpUser wrote:
| Same here. Playing a bit with CLAHE, microcontrast and
| sharpening gives visually the same if not even better results.
| crazygringo wrote:
| Came here to say the same thing.
|
| I was expecting the upscaled image to have extra "invented"
| detail from the supposed ML, as I've seen elsewhere.
|
| But looking at these upscaled images, there isn't any at all.
| There's no extra texture, nothing.
|
| I can't find any difference at all, like you say, from just
| some bicubic interpolation with sharpening.
|
| No jaw dropping here, unfortunately.
| formerly_proven wrote:
| I concur, the second surfer example - the enhanced image looks
| a lot like blowing up the original 2x and applying careful
| sharpening.
| afavour wrote:
| > and applying careful sharpening
|
| That alone makes it worthwhile, surely? No careful
| application required.
| ShamelessC wrote:
| For sure, but the title is quite an overstatement and reads
| like it's from someone who haven't really been paying
| attention to the many existing open source super resolution
| offerings.
| [deleted]
| ISL wrote:
| As a metrologist (and photographer), the difficulty with these
| techniques is that they can over-represent the information
| contained within an image; they present an image of what was
| "probably" there, rather than representing what was. These aren't
| so different from our own brains, which remember what we thought
| we saw, rather than the light that reached our retinas.
|
| These methods are already in extensive use (most smartphone
| images use extensive noise-reduction techniques), but we must be
| ever-cognizant that image-processing techniques can add yet
| another layer of nuance and uncertainty when we try to understand
| an image.
| social_quotient wrote:
| It's like systems that add zeros (floating point math) to the
| right of the decimal. 18.00000 is not the same as 18.
|
| Thoughts?
| dejj wrote:
| You probably want go with 0.1+0.2 != 0.3
| HenryBemis wrote:
| I would say that it's like pixel's RGB at address 1x1 is
| 0-0-0 and pixel at address 1x2 is 0-0-2 and squeezing
| between them a pixel with color 0-0-1 (averaging the two
| values near it)(assuming doing this on a image that has 1
| pixel height and e.g. 2 pixes width; so that the new image
| would be would be:
|
| 1x1 0-0-0 (original)
|
| 1x2 0-0-1 (made up)
|
| 1x3 0-0-2 (original)
| etrautmann wrote:
| No - 000000 is not based on a statistical model of what's
| most likely to be right if the decimal place. In natural
| images - the statistical structure allows for this image
| upscaling but without revealing any previously hidden detail
| - just using know statistics of the world to show what might
| be there.
| gmfawcett wrote:
| That's not how floating point math works? At least not for
| standard floats (IEEE 754), and except for very large
| integers (near 2^m, where m is the mantissa of the FP type).
| Floats have an exact representation for integers within their
| mantissa range -- i.e., '18' is exactly the same as
| '18.0000'.
| genericone wrote:
| I dont think you mean for floating point, but for mechanical
| tolerances. Many times, you dont want to pay an extra $50,000
| for the 5 digits of precision... but sometimes you do. Shitty
| system if it automatically messed up all your part
| tolerances.
| dorkwood wrote:
| An example I saw getting traction on Twitter a few months ago
| was a photo of Melania Trump that was purported to be a body
| double. Since the original image was blurry, someone used an AI
| upscaler to "enhance" the photograph and increase the
| resolution. Then the comments started to roll in: the teeth are
| different! The tip of her nose doesn't match! It's not her!
|
| Technically, they were correct -- it wasn't her. It was an
| algorithm's best-guess reconstruction based on training data of
| other people's faces. Unfortunately, neither the original
| poster or anyone else in the thread seemed to grasp this
| concept.
| roughly wrote:
| I think this point is worth pushing on a bit harder, which is
| to say that the "additional details" in the picture are guesses
| by the software, not actual additional details. The data
| present in the picture is fixed, the software uses that data to
| build educated guesses on what was actually there. If the photo
| doesn't contain enough data to actually determine what a given
| piece of text in the image says, the software can provide a
| guess, but it's just that, a guess. Similarly, if the photo
| doesn't provide enough detail to positively identify a person,
| the "super resolution" one cannot be used to positively
| identify them either, as it's a guess made from incomplete
| data, not genuinely new data.
|
| The point is worth belaboring because people have a tendency to
| take the output from these systems as Truth, and while they can
| be interesting and useful, they should not be used for things
| for which the truth has consequences without understanding
| their limitations.
|
| You're right to compare this to how our brains reconstruct our
| own memories, and the implications that has for eyewitness
| testimony should inform how we consider the outputs from these
| systems.
| TimTheTinker wrote:
| This "guessing" is nice for the sake of artistry, but we've
| got to be careful when knowing what actually _was_ there is
| important--like when photos are submitted as evidence in
| court cases, or when determining the identity of a person
| from a photo as part of an investigation.
| smnrchrds wrote:
| I hope such photos are submitted as camera takes them. With
| our without this new feature, photoshopping a photo before
| presenting it to court must be illegal.
| roughly wrote:
| If you consider photos taken by cell phones, it's hard to
| really say what "as the camera takes them" means - a lot
| of ML-driven retouching happens "automagically" with most
| modern cell phones already and I'd expect more in the
| future.
| kqr wrote:
| It goes even further than that. Image sensors don't
| capture images. They record electricity that can be
| interpreted as an image.
|
| This might seem like a quibble, but once you dive a
| little deeper into it, you realise that there's enormous
| latitude and subjectivity in the way you do that
| interpretation.
|
| What's even crazier is that this didn't come with digital
| photography. Analogue film photography has the same
| problem. The silver on the film doesn't become an image
| until it's interpreted by someone in the darkroom.
|
| There is no such thing as an objective photograph. It's
| always a subjective interpretation of an ambiguous
| record.
| klodolph wrote:
| Analog photography you could at least use E-6. Processing
| was tightly controlled and standardized, and once
| processed, you had an image.
|
| The nice thing about this was that you could hand the E-6
| off to a magazine and end up with a photograph printed in
| the magazine that was very close to the original film.
| Any color shifts or changes in contrast you could see
| just with your eyes. You could drop the film in a scanner
| and visually confirm that the scan looks identical to the
| original. (You cannot do this with C-41.)
|
| This was not used for forensic photography, though. The
| point of using E-6 was for the photographer to make
| artistic decisions and capture them on film, so they can
| get back to taking photos. My understanding is that crime
| scene photography was largely C-41, once it was
| relatively cheap.
| rozab wrote:
| In some use cases, like OCR, the accuracy of these guesses
| can be established in a scientific way. And it tends to be
| very good.
| sanj wrote:
| Unless they're not:
| https://www.dkriesel.com/en/blog/2013/0802_xerox-
| workcentres...
| roughly wrote:
| I agree; I'd say two things in response, though:
|
| 1. However good the guess is, it's still just that: a
| guess. Taking the standard of "evidence in a murder case",
| the OCR can and probably should be used to point
| investigators in the right direction so they can go and
| collect more data, but it should not be considered
| sufficient as evidence itself.
|
| 2. OCR is a relatively constrained solution space - success
| in those conditions doesn't mean the same level of accuracy
| can or will be reached outside of that constrained space.
|
| To be clear, though - I'm making a primarily epistemic
| argument, not one based on utility. There are a lot of
| areas for which these kind of machine guessing systems are
| of enormous utility, we just shouldn't confuse what they're
| doing with actual data collection.
| sweezyjeezy wrote:
| If you're using this to try and enhance super grainy CCTV
| footage to get a face or license plate I'd agree. Purely in the
| context of this article, the author is just upscaling an
| already high-definition image 2x. There's very little artifice
| that can be really added at this level that a human could
| perceive IMO.
| newobj wrote:
| This is perceptual/creative enhance. Not Blade Runner enhance.
| jrockway wrote:
| I think this is probably good for what people use photos for;
| it lets them show a crop without the image looking pixelated.
| That means if they just want a photo to draw you in to their
| blog post, they don't have to take a perfect photograph with
| the right lens and right composition at the right time. And I
| think that's fine. No new information is created by ML
| upscaling, but it will look just good enough to fade into the
| background.
|
| I personally take a lot of high resolution art photos. One that
| is deeply in my memory is a picture I took of the Manhattan
| bridge from the Brooklyn side with a 4x5 camera. I can get out
| the negative and view it under magnification and read the
| street signs across the river. (I would link you, but Google
| downrez'd all my photos, so the negatives are all I have.) ML
| upscaling probably won't let you do that, but on the other
| hand, it's probably pointless. It's not something that has a
| commercial use, it's just neat. If you want to know what the
| street signs on the FDR say, you can just look at Google Street
| View.
|
| (OK, maybe it does have some value. I used to work in an office
| that had pictures blown up to room-size used as wallpaper in
| conference rooms. It looked great, and satisfied my desire to
| get close and see every detail. But, you know you're taking
| that kind of picture in advance, and you use the right tools.
| You can rent a digital medium format camera. You can use film
| and get it drum scanned. But, for people that just need a
| picture for an article, fake upscaling is probably good enough.
| The picture isn't an art exhibit, or an attempt to collect
| visual data. It's just something to draw you into the article
| in the 3 milliseconds before you see a wall of text and
| bounce.)
| danaliv wrote:
| _> These aren 't so different from our own brains, which
| remember what we thought we saw, rather than the light that
| reached our retinas._
|
| Never mind memories; there are parts of our eyes that aren't
| responsive to light at all. We're always hallucinating.
| [deleted]
| prox wrote:
| Would it be right to say it is an synthesis on top of a
| analysis? It wasn't what was observed. For some things it might
| not matter, but "it looks shopped" isn't really a positive in
| my book. Although the use case in the article is pretty handy,
| to print stuff a lot larger.
| [deleted]
| oblio wrote:
| I like how he realizes the impact for pro cameras but doesn't
| highlight the elephant in the room: phone cameras.
|
| This means that soon for many people digital cameras outside of
| their smartphone will become an even more niche product.
| agumonkey wrote:
| I always expected smartphones already used approximation of SR
| to compensate for lower quality optics / ICs.
| t-writescode wrote:
| This is already the case. I rarely have a need to take out my
| SLR - it's just too bulky to have a reason for it, unless I'm
| going on an adventure where photography is one of the or the
| main purpose.
|
| I've gone on hiking trips where my "challenge" was to only use
| my phone camera. It wasn't much of a challenge for landscapes.
| dingaling wrote:
| > It wasn't much of a challenge for landscapes.
|
| Well if you collapse the problem space to a single point that
| corresponds to a phone's standard field of view, then it
| won't be a problem...
|
| But what if you wanted to catch a photo of a rare bird in
| flight at 500mm equivalent, or a surfer caught at 1/4000th of
| a second?
| HenryBemis wrote:
| Most people are like that. But when I go for a 'photowalk' I
| cannot imagine not using my (#1) DSLR (or my (#2) super-duper
| zoom point-and-shoot camera).
|
| Phone (imho) is for quick and dirty, not for a 'it's time to
| do proper photography'.
| Causality1 wrote:
| Any chance of Adobe licensing this tech out? I can see it making
| one hell of a difference when it comes to zoomed-in pictures on
| phones.
| lprd wrote:
| Neat. If only Adobe would do away with their absurd pricing
| models. I'll never use an adobe product again after trying to end
| my subscription with them.
| wlesieutre wrote:
| If you're on a Mac, Pixelmator Pro is a $40 purchase with a
| similar feature
|
| https://www.pixelmator.com/blog/2019/12/17/all-about-the-new...
| Black101 wrote:
| Yeah, a monthly payment forever is ridiculous...
| lprd wrote:
| I think what bothers me the most is that its not just a
| monthly subscription. When you sign up, you are entering a
| one year contract with them. Sure, you can cancel at any
| time...just pay the remaining amount due and you can walk
| away.
| omnimus wrote:
| Exactly. One of the arguments Adobe was making in
| professional circles about the subscription switch was that
| people will save money because they will be able to
| subscribe to each piece of software and for short periods
| of time when they need it.
|
| Truth is that anything other than the full suite (and maybe
| the photographer plan) doesn't make sense financially. And
| then they killed the month by month subscription as you
| said.
| Grakel wrote:
| Absolutely. And the results shown in this article aren't
| particularly impressive. I'll be sticking with photopea.com,
| even though I get free CC through work.
| lprd wrote:
| Creative Cloud was the point I noticed a shift in Adobe's
| priorities. I don't know if they switched CEO's at the time,
| but I starting disliking Adobe more and more from that point
| forward. I couldn't believe the amount of crud Creative Cloud
| puts on your system, not the mention all of the tracking and
| phoning home their software does.
| mcrutcher wrote:
| This seems highly relevant as to what is actually going on:
| http://www.johncostella.com/magic/
| zamadatix wrote:
| For the tl;dr the bit at the end (and linked paper) can cover
| the topic without the backstory if that's not your sort of
| thing:
|
| "As noted above, in 2021 I analytically derived the Fourier
| transform of the Magic Kernel in closed form, and found,
| incredulously, that it is simply the cube of the sinc function.
| This implies that the Magic Kernel is just the rectangular
| window function convolved with itself twice--which, in
| retrospect, is completely obvious. This observation, together
| with a precise definition of the requirement of the Sharp
| kernel, allowed me to obtain an analytical expression for the
| exact Sharp kernel, and hence also for the exact Magic Kernel
| Sharp kernel, which I recognized is just the third in a
| sequence of fundamental resizing kernels. These findings
| allowed me to explicitly show why Magic Kernel Sharp is
| superior to any of the Lanczos kernels. It also allowed me to
| derive further members of this fundamental sequence of kernels,
| in particular the sixth member, which has the same
| computational efficiency as Lanczos-3, but has far superior
| properties."
| tomc1985 wrote:
| A non-tech muggle's jaw hitting the floor is practically par for
| course. I'm so tired of reading these breathless assessments from
| people who don't know any better
| intricatedetail wrote:
| I hate articles where author shows an option but won't actually
| tell where it is located in the application. I spent 10 minutes
| looking for it in latest PS and couldn't find it. Then I clicked
| at link to related article about "Enhance Details" and it seems
| like the option could be in Lightroom instead? I tried to use it
| myself because the illustrations in the article don't look to
| impressive, but authors enthusiasm got me to look for it.
| perl4ever wrote:
| What I felt was missing was a "conventional" enlargement with
| the previous best algorithm side by side with the AI one.
| marcodiego wrote:
| How about a less clickbaity title?
| zarmin wrote:
| oh man, wait til you see the rest of the internet.
| spion wrote:
| So what happens if you take a tiny 10x10 image and run it through
| "super resolution" about 8-10 times?
| sweezyjeezy wrote:
| https://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Su...
|
| stuff like this - assuming it's a GAN under the hood it just
| tries to guess a 'plausible' possible interpolation, but if
| you're giving it very little information about what's in the
| original image, there will be a wide range of plausible images
| it could have arisen from, so the output can be very far from
| the truth.
| Someone wrote:
| I suggest to read the Adobe blogpost at
| https://blog.adobe.com/en/publish/2021/03/10/from-the-acr-te...
| instead. It has sample images side-by-side with bicubic
| upsampling.
|
| Even better comparisons are in the blog post for a competing
| product: https://www.pixelmator.com/blog/2019/12/17/all-about-
| the-new... (likely the same algorithm, but using a different
| training set, so results will be different from what Adobes
| product does).
|
| It has comparisons with nearest neighbor, bilinear and Lanczos
| filters and uses a slider to make it easier to see the
| difference.
|
| Papers on this task: https://paperswithcode.com/task/image-super-
| resolution
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(page generated 2021-03-13 23:01 UTC)