# This AI Performs Super Resolution in Less Than a Second

## Метаданные

- **Канал:** Two Minute Papers
- **YouTube:** https://www.youtube.com/watch?v=HvH0b9K_Iro
- **Дата:** 06.09.2018
- **Длительность:** 3:46
- **Просмотры:** 109,553
- **Источник:** https://ekstraktznaniy.ru/video/14419

## Описание

The paper "A Fully Progressive Approach to Single-Image Super-Resolution" is available here:
http://igl.ethz.ch/projects/prosr/

A-Man's Caustic scene: http://www.luxrender.net/forum/gallery2.php?g2_itemId=27260

Corresponding paper with Vlad Miller's spheres scene:
https://users.cg.tuwien.ac.at/zsolnai/gfx/adaptive_metropolis/

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## Транскрипт

### Segment 1 (00:00 - 03:00) []

dear fellow scholars this is two minute papers with károly John I fail when looking for illustrations for a presentation most of the time I quickly find an appropriate photo on the Internet however many of these photos are really low resolution this often creates a weird situation where I have to think okay do I use the splotchy er lower resolution image that gets the point across or take a high-resolution crisp image that is less educational in case you are wondering I encountered this problem for almost every single video I make for this channel as you can surely tell I am waiting for the day when super resolution becomes mainstream super resolution means that we have a low resolution image that lacks details and we feed it to a computer program which hallucinates all the details onto it creating a crisp high resolution image this way I could take my highly relevant but blurry image improve it and use it in my videos as adding details to images clearly requires a deep understanding of what is shown in these images are seasoned fellow scholars immediately know that learning based algorithms will be ideal for this task while we are looking at some amazing results with this new technique let's talk about the two key differences that this method introduces one it takes a fully progressive approach which means that we don't immediately produce the highest resolution output we are looking for but slowly leapfrog our way through intermediate steps each of which is only slightly higher resolution than the input this means that the final output is produced over several steps where each problem is only a tiny bit harder than the previous one this is often referred to as curriculum learning and it not only increases the quality of the solution but is also easier to Train solving each intermediate step is only a little harder than the previous one it is a bit like how students learn in school first the students are shown some easy introductory tasks to get a grasp of a problem and slowly work their way towards mastering a field by solving problems that gradually increase in difficulty - now we can start playing with the thought of using a generative adversarial Network we talked a lot about this architecture in this series at this time I will only note that training these is fraught with difficulties so every bit of help we can get is more than welcome so the role of curriculum learning is to help easing this process note that this research field is well explored and has a remarkable number of papers so I was expecting a lot of comparisons against competing techniques and when looking at the paper and the supplementary materials boy did I get it make sure to have a look at the paper it contains a very exhaustive validation section which reveals that if we measure the error of the solution in terms of human perception it is only slightly lower quality than the best technique however this one is 5 times quicker offering a really nice balance between quality and performance so what about the actual numbers for the execution time for instance up sampling an image to increase its resolution to twice its original size takes less than a second and we can go up to even eight times the original resolution which also only takes four and a half seconds the quality and the execution times indicate that we are again one step closer to mainstream super resolution what a time to be alive the source code of this project is also available thanks for watching and for your generous support now see you next time