How Super Resolution Works

[@utkarshdeshmukh3268]

This is one of the best videos I have seen so far related to super resolution. It explains a fantastic summary of many papers. Great work Leo!

[@leoisikdogan]

Thanks, Utkarsh!

[@harshitpandey638]

I think I hit a gold mine by finding this channel

[@kartikpodugu]

I saw this video a year back with limited deep learning knowledge, but still understood the concepts at a higher level. I understood that super resolution is possible with deep neural networks. Now watching this video with more knowledge about different deep neural network architectures, gave me more understanding.

[@dvirzag]

One of the greatest, combined many courses with no realism to a daily operations using all of them theorems. Well done! :)

[@user-maymay2002]

damn, one of the best SRGAN & ECRGAN explanation vids out there! It was straight to the point and the way of explaining was flawless. Thanks!

[@_RMSG_]

4:16 I think it does, if the hair is moving in the wrong direction, and the picture is sharp enough, it will look distinctly machine-generated, so perceptually it does matter, but not _necessarily_ for noise reduction

[@tazmeenfatima98]

Wow, love how you are explaining what techniques and what papers use them, along with excellent details of super-resolution its self.

[@poshko41]

The amazing thing is that this is only going to keep getting better as more people use it.

[@chadyonfire7878]

nice very general view of the topic , we need more of this

[@minthway2736]

This video is very effective for me. Thank You, Leo. You are the best👍

[@tonyjames9929]

The explanation about Data Processioning inequity was super, thank you (Y)

[@yafesenessahiner4782]

Awesome context, thank you for all of them. Great job!

[@Dottor_J]

Your videos are always very interesting, thanks Leo

[@bhaveshgohel]

'Super' video of Super Resolution. 👌

[@emresafter8629]

Just rushed into the topic and got scared by dense papers. Luckily I found this video and I feel much more familiar with the concept. Thank you for great summary and explanation!

[@myrainys]

Give this man a medal.

[@cheng-tsuyu7300]

Your explanation is very concise and clear! It really helps me in my deep learning class. Thank you so much!

[@antoinec5070]

Fantastic video ! Summarising some papers so good

[@이종학-t1j]

how interesting! thanks for your explanation :) I clicked the 'good' bb

[@kaoutharaarizou1914]

Very nice overview ! Super-resolution is my ph.d researche field, and i am more interested in task-specific SR

[@TheAkbar1000]

Thank you for this extensive review paper style video... It really helped me and I am sure it will also help every other undergrad like me who is looking to attempt Super Resolution. I have studied some papers online and was trying to figure out their place on the timeline and what are the current state-of-the-art architectures. I had also planned to investigate how all of this related to the famous Google Computational Photography but you have provided a fine overview... Thanks for the links... I will be looking forward to more of your videos...

[@hammerchu948]

Amazing summary, great video~!

[@CodeEmporium]

Nice! Gonna read up more on your references for this

[@AkhilBabel]

Love your videos Leo. Please keep it up.

[@superaluis]

Thanks for this great summary video. Best updated video on this topic for sure!

[@yasinilyas2134]

Thanks for the input!

[@randomname4726]

Excellent video!

[@DinJerr]

Can we avoid using the term 'hallucinate' to describe detail generation? Hallucination is conjuring up images without any external stimuli causing it. In the case of super resolution, you definitely need the current pixel information to determine what gets drawn in the inbetween space, and in the case of GAN, the model draws upon what images it was trained on to create those detail. None of these come out of 'nowhere', so by dictionary definition hallucinate is a wrong word to use. Some parties use the term 'dream' instead, and it's a bit more appropriate as it draws upon the knowledge/memory of the dreamer when making up its details.

[@leoisikdogan]

That makes sense. I didn't come up with the usage of the word hallucination in the context of generative neural networks though. It's been widely used for methods that generate information that cannot be inferred from the input alone.

[@TheAkbar1000]

I believe that an illusion is the miss interpretation of sensory information, and hallucination is the addition of extra details which are not sensed by our sensory organs but are added by our brains... In that sense, this word seems quite appropriate for the action that it describes.

[@RemyRAD]

I am incredibly intrigued by Super Resolution. And while it is a great descriptive explanation of what, Super Resolution is. Other than the academic bookwork. How am I to get this for my historic, VHS, 4 x 3 productions. However when cropping 260 line resolution, color under video to, 16 x 9. I'm reducing that 260 lines of resolution to something like, 175-200 lines of resolution. Along with the need to bump it to 1080 P at 16 x 9. And I have some interesting and pleasant results using Vegas video editor. But not to the extent of this Super Resolution, process. So I'm preparing to archive some videos of a former Metropolitan Opera star from the late 1940s. With videos of her from the 1980s on VHS. And which already contains an excellent hi-Fidelity soundtrack in stereo. And would be much nicer to make the video look professional and acceptable. So your explanation and description is great. How do I do it? How do I get it in video? One frame at a time? Like Photoshop? Or, because my mathematical aptitude sucks so badly. This might be out of the realm of my capabilities? And it will just have to look amateur VHS fuzzy. Until this process is refined and becomes a single click plug-in. In your choice of video editing software. But not quite there yet. Oh well. I'll just have to wait a long time to come before that happens. At least a month or two. And what kind of resolution will we see. If Donald Trump gets reelected? As clear as shit. That's what. Clearing the way for 4 more years of insane shit. I vote for Super Democracy Resolution. We need an enhanced democracy with increased resolution. And political transparency. If that were only possible? I'm going to vote for increased democratic resolution RemyRAD

[@leoisikdogan]

Wow, I have no idea how to reply to this :) As for the VHS footages, EDVR (github.com/xinntao/BasicSR) may work well. I haven't tried it but it seems to do a good job on video super resolution. As for the increased democracy resolution, I'm not a US citizen yet, therefore cannot vote. So, whomever Americans want to see in the oval office, I respect their choice.

[@harsha.n9332]

You are the most underrated guy.... This is too much for me though 😅

[@lesleyeb]

Great summary! Thanks!!

[@ttrkaya]

Enjoyed a lot! Thanks for the well-made video.

[@hsiang-yehhwang2625]

Nice!! Thanks for the sharing!!

[@parsamadayeni]

Does the procedure affect the respawn time?

[@Rose-qs2gy]

Sir, can I do a super resolution project as my academic project,

[@morgan3913]

Can you make a video summarizing some methods of applying computational photography techniques to smartphone cameras? More specifically, approaches that allow for more manual inputs compared to the automatic features found in the Google Pixel devices. I would love to see a video centered around the idea of capturing sensor data with a smartphone camera with the intention of using resource intensive techniques on a separate more powerful computer to process the image.

[@leoisikdogan]

The basic computational photography techniques that smartphones use are similar to the ones that other digital cameras use. I made a video about that earlier: kzbin.info/www/bejne/aXabdZ-BjrChhZY Beyond those basic processing methods, Google Pixel and other advanced smartphone cameras use more sophisticated burst image processing methods to make up for the shortcomings of a small imaging sensor. I might cover those methods in a future video.

[@morgan3913]

@@leoisikdogan I did see that video and as an explanation of the theoretical framework for image processing pipeline it was very good. However, I would like to see ways to apply computational photography techniques if one was to take a more manual approach as opposed to relying on the manufacturers implementation. This topic is relatively new to me so I may not be asking the correct questions. Basically, how much further can you push image quality of mobile photography beyond the manufacturer first party application?

[@leoisikdogan]

@@morgan3913 You might find Marc Levoy's work interesting. He has been working on this topic for quite some time and now is leading Google Pixel's camera team. Some of his papers: Burst photography for high dynamic range and low-light imaging on mobile cameras graphics.stanford.edu/papers/hdrp/hasinoff-hdrplus-sigasia16-preprint.pdf Pixel Night Sight ai.googleblog.com/2018/11/night-sight-seeing-in-dark-on-pixel.html Handheld Mobile Photography in Very Low Light arxiv.org/pdf/1910.11336.pdf There are also these papers from Intel Labs (not my team though): Learning to See in the Dark openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Learning_to_See_CVPR_2018_paper.pdf Seeing Motion in the Dark vladlen.info/papers/DRV.pdf I guess even those papers alone have enough material for a new video :)

[@watercui9346]

Very clear, bro, thank u.

[@ihsankaratas2655]

Thanks Leo. You are best

[@Phobos11]

In the gaming world, we don't use the esrgan model for real images, as assumed in the video, we have trained our own purpose-built models for different cases

[@leoisikdogan]

As far as I know, the early examples used ESRGAN trained on natural images. I would be interested to see the results of models trained on video game graphics. Do you have any checkpoints for such models?

[@Phobos11]

@@leoisikdogan yes, you can find our public models in the database at upscale.wiki/wiki/Model_Database . More information and showcases in the subreddit (www.reddit.com/r/GameUpscale/), the Discord channel (discord.gg/cpAUpDK) and some custom modifications and tests to the BasicSR codebase in my fork at github.com/victorca25/BasicSR .

[@leoisikdogan]

Thanks for the links. It looks like the community has grown a lot. There are many model checkpoints fine tuned for different tasks.

[@Phobos11]

@@leoisikdogan Indeed, we worked on many different cases and a parallel group grew besides the game upscale one, more focused on animation upscaling. We ended up creating models for many tasks, in my fork I included the code to automatically augment images with different types of degradations, but the other guys did the hard work of creating the datasets and training the models. I've been unable to participate for a while, but it's a great community, last time I was there, there was interest in DAIN as well.

[@ddkgurumani4770]

Very informative

[@bucoescobar1961]

Very good video ! I came to this from some Convolution Neural Network videos while I was googling AMDs new Super Resolution Feauture (competitor to nvidias DLSS). Also did anyone ever tell you that you look like Quentin Tarantino‘s son? :D

[@leoisikdogan]

Haha, yes I do get that sometimes :)

[@polaris911]

what if you put the upscaled image into the ESRGAN input and upscale again? INFINITE RESOLUTION!

[@leoisikdogan]

Upscaling artifacts become more visible every time you upscale. It seems to produce decent results for up to 16x upscaling in both axes.

[@william_SMMA]

@@leoisikdogan nice

[@sourabhbhattacharya9133]

there should be a super like option in KZbin for contents like this

[@AustinNGrayson]

If a game plays in native 4K is there any reason to even turn it on? Does it cause input lag if I do turn it on? I have it on low right now in game mode.

[@jec_ecart]

That madr my head spin a bit. However it's cool !👍

[@aksshaysharma96]

Does implementation of super resolution in a very rudimentary way implementation of AI?

[@marwanssalem]

what about video super resolution

[@abhiigg]

nvidia is on that

[@shahrinnakkhatra2857]

Can't we just create synthetic burst of images to recreate google's one?

[@nasereddinehafidi7220]

Interesting results

[@redaabdellahkamraoui7325]

outstanding !

[@yusufsevinc609]

thank you

[@ritwek98]

thanks...

[@GeoffHolman]

What's your view on the image processing experts in the Kyle Rittenhouse trial? I would love to see a review of the how accurate the expert advice was.

[@Kidbuuisstrongerthanbroly]

This is fucking incredible

[@umutcelik9821]

abi türk müsün

[@benbar5449]

This video has only 5k views? wtf.

[@vijayanand8217]

wow

[@eyescreamcake]

The information in the training set does not count as information in the sense of data processing inequality. This is not actual super resolution; it's just educated guessing. All the details in *all* of these examples are completely made-up.

[@XX-vu5jo]

What happened to this channel???

[@RetroBulgaria]

I like when somebody proudly speak about thinks he do not understand. Is it talking about gay resolution?

[@BenderdickCumbersnatch]

Someone speaking about things he does not understand? Ah, you are speaking about yourself, Markov.

[@RetroBulgaria]

This from your perpspective. Because you do not understand NOTHING from this matter, you listen and say "Waw this guy is genius" but he only is "genius" in the blind eye of peoples that understand nothing like you. For peoples that seriously work in this domain, this "presentations" is dummy funny.

[@BenderdickCumbersnatch]

@@RetroBulgaria Why are you so mad bro?

[@RetroBulgaria]

Because I'm tired of amateurs (claim to be professionals), dummies, morons, stupid peoples etc. that filled western world.

[@BenderdickCumbersnatch]

Your over-reaction to a nicely explained summary video is insane. And you called him gay for no reason and without proof. You seem to be both rude and stupid at the same time. ;-)

[@vladimirbosinceanu5778]

Great video!