DeOldify can Colorize your Black & White Photos with Full Photorealistic Renders

DeOldify is a technique to colorize and restore old black and white images or even film footage. It was developed and is still getting updated by only one person, Jason Antic. It is now the state-of-the-art way to colorize black and white images. Best of all, everything is open-sourced!

Watch the video

References

1. What are GANs: ​
2. GitHub with full code, in-depth explanation, and Colabs: ​
3. Free DeepAI image colorization API using DeOldify: ​
4. MyHeritage colorization tool (the best version of DeOldify available, paid): ​

Follow me for more AI content:

• Instagram: ​
• LinkedIn: ​
• Twitter: ​
• Facebook: ​
• The best courses in AI: ​
• Join Our Discord channel, Learn AI Together: ​

Chapters

0:00​ - Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.

0:40​ - DeOldify explanation

3:22​ - How to use DeOldify Yourself

4:24​ - Old Movie Examples

5:06​ - Conclusion

Video transcript

00:00

this ai can colorize and restore old

00:03

black and white images and even film

00:05

footage

00:06

this method is called de-aldefy and

00:09

works on pretty much

00:10

any picture if you don't believe me you

00:13

can even try it yourself for free as i

00:15

will show in the video

00:17

but first let's see how it works and

00:19

more amazing results to convince you to

00:22

try it

00:25

[Music]

00:29

this is what's ai and i share artificial

00:31

intelligence news every week

00:33

if you are new to the channel and want

00:34

to stay up to date please consider

00:36

subscribing to not miss any further news

00:40

the aldephi is a technique to colorize

00:42

and restore

00:43

old black and white images or even film

00:45

footage

00:46

it was developed and is still getting

00:48

updated by only one person

00:50

jason antique it is now the

00:53

state-of-the-art way to colorize black

00:55

and white images

00:56

and everything is open sourced but we

00:59

will get back to this in a bit

01:01

first let's see how he achieved that it

01:04

uses a new type of gun training method

01:07

called nogan that he developed himself

01:09

to solve the main problems that appeared

01:11

when training using a normal

01:13

adversarial network architecture

01:15

composed of a discriminator and a

01:17

generator

01:18

typically gun training works by both

01:21

training the discriminator and generator

01:23

at the same time

01:24

where the generator starts by being

01:26

completely random

01:27

and improves over time to fool the

01:29

discriminator

01:30

which tries to tell if the image is

01:32

generated or real

01:34

if this was just completely abstract to

01:36

you i invite you to watch the video i

01:38

made about cans

01:39

in the upper right corner right now and

01:41

linked in the description

01:43

before continuing this video his new

01:46

method

01:46

which he calls the nogan provides the

01:49

same benefits of this usual gun training

01:52

while having to spend way less time

01:54

training the gan architecture

01:56

which is typically pretty heavy in

01:58

computation time

01:59

instead he pre-trains the generator to

02:02

make it already more powerful

02:04

fast and reliable using a regular loss

02:07

function

02:08

this is done by training the generator

02:10

like a regular deep networks

02:12

architecture

02:13

such as resnet that way the model is

02:16

already pretty good at colorizing an

02:18

image

02:18

before training the complete gan

02:20

architecture

02:22

then it only needs a short amount of

02:24

this typical generator discriminator

02:26

gan training to optimize the realism of

02:29

the generated pictures

02:32

gaussian noise is also randomly applied

02:34

to images to generate

02:36

fake noise during training this is a

02:39

type of data augmentation that can be

02:41

performed on the training images to

02:43

improve the results and resistance

02:45

to noisy inputs using the same technique

02:48

as style transfer where the noise will

02:50

be the style of the image we want to

02:52

copy

02:53

and can be applied more or less to the

02:55

transformation

02:57

the whole architecture uses a basic

02:59

resnet backbone

03:00

on a u-net where the generator network

03:02

in the gun training

03:04

is the unet architecture right now

03:07

there is no complete explanation of how

03:09

this works

03:10

but the author is currently working on a

03:13

paper about

03:14

the aldephi where he will further

03:16

investigate

03:17

why and how his technique previously

03:20

found only by trials and errors

03:22

work you can find three things in the

03:25

description of the video

03:27

at first there's the github link with a

03:30

complete detailed explanation of the

03:31

technique and even google collab

03:33

tutorials

03:34

to use it yourself just look at how

03:37

simple this is

03:38

you can just run the few sections enter

03:41

the link of your image

03:43

and run it

04:04

then you can find a free api on deep ai

04:08

using dl defy where you can simply click

04:11

and try yourself

04:13

finally the third link is the most

04:15

advanced version of the aldify if you

04:17

are looking for the best results

04:20

it is on myheritage's website and is

04:23

paid to use

04:24

let's just take a minute to see how it

04:26

works on old movies before ending this

04:40

video

04:50

you better get on the job some of the

04:52

kids may be up this afternoon

04:53

oh jack we can get along without

04:55

dragging those young kids up here oh why

04:57

don't you button up your lip

04:59

you're always squawking about something

05:00

you got more static on the radio

05:07

please leave a like if you went this far

05:09

in the video

05:10

and since there are over 90 of you guys

05:13

watching that are not subscribed yet

05:15

consider subscribing to the channel to

05:17

not miss any further news clearly

05:19

explained

05:20

thank you for watching

05:23

[Music]