Compressed Sensing: Overview

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This video introduces compressed sensing, which is an exciting new branch of applied mathematics, making it possible to reconstruct full images from a random subset of the pixels. There is a ton of beautiful math behind this concept, touching on high-dimensional geometry, robust statistics, and optimization.
Book Website: databookuw.com
Book PDF: databookuw.com/...
These lectures follow Chapter 3 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: www.amazon.com...
Brunton Website: eigensteve.com
This video was produced at the University of Washington

Опубликовано:

7 сен 2024

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Комментарии : 66

@elromulous 3 года назад

It's rare to find a lecturer who explains complex topics so lucidly! Thank you!

@filipee77 3 года назад

Amazing. I use CS in my PhD here from Brazil. Great explanation and work.

@Eigensteve 3 года назад

Thanks!

@somethingnew7538 3 года назад

I really love the way you teach, And The famous line "so on and so forth".♥️

@yasir9909 3 года назад

You have presented a very good, concise, and easy-to-understand description of the subject matter for an average student...

@Eigensteve 3 года назад

Thanks!

@MissyMcFruit 3 года назад

These videos are so helpful as I am writing my master's thesis in Compressed Sensing! So many books and articles are so technical, that it's hard to see the woods for the trees. Love your teaching style!

@zieglerai 3 года назад

I have been working with compressed sensing in my bachelor thesis and a few months ago I couldn't find a collection of videos explaining CS like you are doing here very easily and interactively. Thank you for taking the time to make and upload this great content!

@mohammedsaad7122 Год назад

first of all thank you so much for the in-depth explanation. This is literally whats gettting me through Compressed sensing Class. Secondly, its mind blowing to see how easy mirrored writing is for you.

@Veptis 2 года назад

I wonder if I any course at my university touched on this topic. If not I might watch all the material on your channel. It's really interesting as I remember the idea from an old forum post for the use of thermal imaging. Which is quite low resolution and really noisy. Also impressive to see you writing everything mirrored and making it look effortless

@franciscojavierramirezaren4722 3 года назад

Thanx for sharing, great book! Greetings from México!🙂

@medad5413 2 года назад

Thank you for making the book and code available.

@craigcollings5568 2 года назад

Databook downloaded. Also, Sparse identification of nonlinear dynamical systems

@Blakut 3 года назад

He circled s using the l-inf norm

@zonglehuang4147 2 года назад

Video with high quality! Really helps a lot.

@slevon28 3 года назад

Great job. Your book will definitely be a reference in my PhD-Thesis! Thank you very much.

@yaakoubberrgio5271 3 года назад

Hello can you find me matlab code of compressed sensing 1d and 2d Thanks

@Kracheta 3 года назад

OMG this is my new favourite channel! You explain so well, thank you!

@EduardoGarcia-tv2fc 4 года назад

What a great book and series!

@yanhairen7293 3 года назад

This lecture is so good. Enjoy it so much

@duffahtolla 3 года назад

I wonder if this could give you a preview of long exposure digital photographs used in astronomy.

@pablogiuliani4618 2 года назад

This is fantastic, thank you so much Steve and Nathan from the massive amount of work that went into writing the book and making these videos. BTW, I was wondering if you learned to write in an inverted (flipped) fashion just for the sake of these videos, but then I thought most likely you wrote normally and then flipped the video itself. The position of your ring says that is most likely the correct answer ;).

@fzigunov 3 года назад

Can't wait to see the math!!

@taimurzaman7322 Год назад

Damn it, you gona giving me my Ph.D. so easily :) see how his books are available for free.

@alfonsollanes888 Год назад

Magnificent lectures about image compression. I have a question about extracting distance between and within images from a flat photo image. Do we have the projective geometry math available for this process or is flat photography need to depend on technology like LINAR?

@manuelpena3988 3 года назад

This teaser really has me waiting for the next video. It never happened to me with a movie xD

@rickharold7884 3 года назад

Can’t wait ! Thx

@INDIANHeroes 3 года назад

Can anyone tell how to verify the results obtained from compressed sensing are how much accurate ?

@TheKhakPakflyest 3 года назад

Fantastic as usual.

@hrtlsbstrd 3 года назад

Love your communication style, a lot of professors could do well to learn from you.

@greje656 3 года назад

Really interesting. You are a fantastic presenter btw :)

@zrmsraggot 2 года назад

Hello, could it be possible to do it within a movie ? Having a representation of the climate with only a few measurements over a continent and then being able to predict how it will evolve would be great ! Also I wonder if randomness is optimal for Compressed Sensing, is there a way to tell if some measurements are better than others ? Thanks to anyone who read this and takes time to answer

@existenence3305 3 года назад

Turns out, Steve is an AGI that came from the future to teach us about how to build him.

@MaksymCzech 3 года назад

Great stuff, just like always!

@zhihuachen3613 3 года назад

really useful, thank you professor

@kansasmypie6466 2 года назад

I can’t believe all of this is for free. That’s too good to be true lol

@EEDKonduruLakshmiBhanuPrakashR 3 года назад

hi sir, as specified in video X=basis function(Fourier)*S(sparse) that sparse matrix contains already the less number of non zero pixels and maximum of zeros. how we will get that sparse matrix vector of image the multiplication of basis and sparse is equal to X. so that we can write it as Y = MEASUREMENT MATRIX)*(BASIS_FUNCTION)*(SPARSE IMAGE) i am confusing how to get that sparse image and how to design measurement matrix professor can you please clarify my doubts. thank you professor

@odlz3636 3 года назад

Many thanks !

@nickey0207 3 года назад

It looks like your lower body is not in R(A) in the video. We might recover it tho.

@INDIANHeroes 3 года назад

Does compressed sensing uses matrix multiplication anywhere in its implementation?

@Eigensteve 3 года назад

A lot of the algorithms involve iterative solvers, each step of which often involve some sort of matrix inversion. Can get pretty expensive.

@yaakoubberrgio5271 3 года назад

Hello steve Can you find me matlab code of compressed sensing Thanks

@jajula2565 3 года назад

@yaakoub Berrgio can u share ur mail contact to reach u jsrece87@gmail.com

@mariahanveiga2478 3 года назад

your videos are so great!

@er.dharnabakotra8849 3 года назад

Respected sir, May I have MATLAB code for practical purpose for checking and making image sparse?

@sj.j5169 3 года назад

Is it necessary that y be a [p , 1] vector or one could use y as a [p, l] matrix? If I had a two dimensional matrix as y, should I re-arrange it as a long vector or use it directly?

@charismaticaazim Год назад

From what i understand, its a matter of convenience in computation, due to which its converted to vector (1d). Is this convenience necessary ? For now, my guess would be yes, to ensure compatibility.

@prashantsharmastunning 3 года назад

if we use actual row resolution image in place of randomly sparse matrix,. will we be able to high res it ?

@yaakoubberrgio5271 3 года назад

Hello Can you find me matlab code of compressed sensing Thanks

@neb5615 3 года назад

Thank you

@pepaxxxsvinka3379 3 года назад

Nice video! I am trying to understand this method. Can I resize the image using CS?

@mamatoshgupta 2 года назад

Re-frame your problem as follows: The image that you have is a sparse sampling of a higher resolution image which you want (resized image). Then apply the CS reconstruction theory with the appropriate compression matrix.

@anilkumar-kv4we 3 года назад

Thank you sir

@kopapa 2 года назад

Thanks for this compact overview. Btw: That “Infinitely many ass*s” made me giggle.

@jamescarson668 Год назад

Love this series. @SteveBrunton is awesome! Just want to throw out to be helpful, "Beg the question" should be "raises the question." The former is a way of pointing out a fallacy: en.wikipedia.org/wiki/Begging_the_question. I used to make the same mistake and was happy when someone corrected me.