PULSE is a new image upsampling algorithm. This new algorithm transforms a blurry image into a high-resolution image. It can take a super low-resolution 16x16 image and turn it into a 1080p high-definition human face! You don’t believe me? Then you can try it yourself in less than a minute using their demo. Watch the video References Paper: Demo: Github: https://lnkd.in/gUdHFJs https://lnkd.in/gmN3jTq https://lnkd.in/gp5paYb Video Transcript 00:00 this new algorithm transforms a blurry 00:02 image to a high-resolution image it can 00:04 take a super low resolution like a 16 by 00:07 16 image and turn it into a 1 ADP High 00:10 Definition human face you don't believe 00:12 me then you can do just like me and try 00:14 it yourself in less than a minute but 00:17 first let's see how they did that this 00:25 is what they I and I share artificial 00:27 intelligence news every week if you are 00:29 new to the channel and want to stay 00:31 up-to-date please consider subscribing 00:33 to not miss any further news to start we 00:36 have to introduce the concept of photo 00:38 up sampling our image super resolution 00:40 the goal here is to construct a high 00:43 resolution image from a correspondingly 00:45 low resolution input which is a face in 00:48 this case the low resolution will be 00:49 such as 16 by 16 pixels super blurry to 00:53 a high definition on one a DP image with 00:56 a clear face usually these techniques 00:58 use supervised learning to trained our 01:00 network and measure the average distance 01:02 between the new high-definition image 01:04 and a high resolution ground truth but 01:07 using this technique seem to neglect 01:08 important details like textures and 01:11 create some blurry spots when the answer 01:13 is uncertain because of the differences 01:15 between the low and high resolutions 01:17 this is where polls comes and play their 01:20 goal was to generate realistic images 01:22 within the set of plausible solutions 01:25 meaning that they wanted to rely on an 01:27 actual image that was realistic where 01:30 it's downscale version will look the 01:32 same as the original low resolution 01:34 image instead of having to guess 01:36 directly from the low resolution image 01:38 so they introduced a new self supervised 01:41 technique that traverses the high 01:43 resolution natural image manifold 01:45 searching for images that don't scale to 01:48 the original low resolution image this 01:51 is great because as you can see in this 01:53 image multiple high resolution images 01:55 can be done scaled to the same low 01:58 resolution image which will cause a 02:00 blurry uncertainty with previous 02:02 techniques these high resolution images 02:04 are made by a gun Network which is pre 02:07 trained in an unsupervised way to 02:09 generate multiple realistic and sharp 02:11 face images 02:12 if you are not familiar with self 02:14 supervised learning or generative 02:16 adversarial networks or Ganz I invite 02:18 you to watch the video I introduced them 02:20 both are linked in the description below 02:22 at first I was skeptical probably just 02:25 like you so I decided to try it myself 02:28 since the code is available and the 02:30 results were amazing you can set the 02:33 image is super blurry no one could guess 02:35 how I really looked like in this picture 02:37 and yet even though I don't think it 02:40 looks just like me it's still extremely 02:43 close to the reality for the amount of 02:45 information it was fed you can even try 02:48 it on yourself right now with the demo 02:49 no setup needed and it takes only a 02:52 minute or so click on the link in the 02:54 description run the program enter your 02:57 image which will not be saved by them 02:59 for privacy and here are the results in 03:02 this work they used a Celebi HQ data set 03:05 which is a large scale face attribute 03:07 data set with more than 200 thousand 03:09 celebrity images this could explain why 03:12 the faces look older and different than 03:15 non celebrity faces it will be 03:17 interesting to see a comparison with 03:19 another large data set using a different 03:22 segment of the population and this work 03:24 is of course generalizable and could be 03:26 of great use in fields like medicine 03:29 astronomy or satellite imagery where 03:32 sharp and high-resolution images are 03:34 difficult to have due to their cast or 03:36 hardware and memory limitations of 03:38 course this was just a simple overview 03:41 of the pulse photo op sampling algorithm 03:43 I strongly recommend to read the paper 03:46 and play with the code both are linked 03:48 in the description for more information 03:49 leave a like if you went this far in the 03:52 video and since there are over 90% of 03:55 you guys watching that are not 03:56 subscribed yet please consider 03:58 subscribing to the channel to not miss 03:59 any further news clearly explained
